system

Abstract

We provide the system. [Solution] A sensor means for acquiring biological data during sleep in real time, An analytical means for analyzing acquired biological data and determining sleep stages, An environmental adjustment means that adjusts the physical environment according to the determined sleep stage, A health monitoring system that monitors health status based on sleep data and detects abnormalities, A suggestion generation method that generates suggestions to improve the efficiency of the day's activities upon waking up, A recommendation generation method that generates recommendations to optimize lifestyle habits based on lifestyle data, A system that includes this.

Description

Technical Field

[0004] , ,

[0005] , ,

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, it is a problem that many people are troubled by a decline in the quality of sleep and a consequent decline in performance during waking hours. In particular, it is difficult to maintain an appropriate sleep environment and it is impossible to detect health risks during sleep at an early stage. Solving this problem contributes to extending the healthy life span and improving the quality of daily life.

Means for Solving the Problems

[0005] To address this challenge, the present invention provides a sensor means for acquiring biological data during sleep in real time, and an analysis means for analyzing the acquired biological data and determining the sleep stage. Furthermore, by using an environment adjustment means that adjusts the physical environment according to the determined sleep stage, it is possible to provide an optimal sleep environment. In addition, by using a health monitoring means that monitors the health status based on sleep data and detects abnormalities, it is possible to detect health risks early. Finally, it provides a suggestion generation means that generates suggestions to improve the efficiency of the day's activities upon waking, thereby improving the user's quality of life.

[0006] A "sensor means" is a device or mechanism that acquires biological data during sleep in real time, and measures data including brain waves, heart rate, respiratory patterns, and external environmental data.

[0007] "Analysis means" refers to a device or mechanism for analyzing acquired biological data in real time and determining the user's sleep stage.

[0008] "Environmental adjustment means" refers to a device or mechanism for automatically adjusting the physical environment of the bed, such as its hardness, temperature, and tilt, according to the determined sleep stage.

[0009] A "health monitoring device" is a device or mechanism that monitors health status based on sleep data and is used for the early detection of health risks or the detection of abnormalities.

[0010] "Suggestion generation means" refers to a device or mechanism for generating personalized suggestions upon waking up to improve the efficiency of activities on the day. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention is a system for acquiring biometric data in real time during a user's sleep and providing an optimal sleep environment based on that data. This system consists of several key components that work together to provide the user with high-quality sleep and improve their activity efficiency after waking up.

[0033] First, the device acquires data on the user's brainwaves, heart rate, breathing patterns, and external environmental data such as room temperature and illuminance through a group of sensors installed on the bed. This data is transmitted to the server in real time.

[0034] The server utilizes machine learning algorithms to analyze the received biometric data. These algorithms accurately determine the user's sleep stage and provide a basis for determining the most suitable sleep environment. Based on these results, the server calculates environmental adjustment parameters and sends instructions back to the terminal.

[0035] Next, the device adjusts the physical characteristics of the bed (firmness, temperature, and tilt) according to instructions from the server. This allows the user to rest in an optimal environment for each stage of sleep. Because this adjustment is made flexibly according to the depth of sleep, the user can enjoy higher quality sleep.

[0036] In addition, the server continuously monitors sleep data and generates alerts to warn of health risks if abnormal data patterns are detected. These alerts are automatically sent to the user's smartphone, prompting them to consult a medical professional if necessary.

[0037] Furthermore, the device reviews the user's sleep data and daily schedule each morning to provide personalized advice to maximize activity efficiency. This advice goes beyond mere recommendations, serving as a guide for the user to have a healthy and productive day.

[0038] As a concrete example, suppose a user experiences a sudden increase in heart rate during normal sleep. In this case, the server immediately detects this anomaly and sends an alert to the user. Simultaneously, the device adjusts the firmness and temperature of the bed to help the user transition from deep sleep to light sleep. The following morning, the user is provided with advice on how to improve their activity efficiency, taking this anomaly into account. Thus, the present invention is a system that comprehensively supports the quality of a user's sleep.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device acquires brain waves, heart rate, breathing patterns, and room temperature and illuminance from sleep sensors. This data is collected in real time at regular intervals.

[0042] Step 2:

[0043] The device transmits the acquired biometric data to the server. The data is encrypted and sent through a secure communication channel.

[0044] Step 3:

[0045] The server processes the received data through an analysis algorithm to determine the sleep stage (light sleep, deep sleep, REM sleep). This analysis also takes into account past sleep data to derive the optimal analysis result.

[0046] Step 4:

[0047] The server calculates the optimal environment settings based on the analysis results. These settings include bed hardness, temperature, and incline.

[0048] Step 5:

[0049] The terminal receives the environment settings sent from the server and adjusts the bed's physical mechanisms. Specifically, it activates the built-in motors and temperature control devices to apply the settings.

[0050] Step 6:

[0051] The server continuously monitors the data and sends an alert to the user if it detects an abnormal data pattern that exceeds a specified range (e.g., a sudden increase in heart rate). The alert is displayed on a smartphone or bedside display.

[0052] Step 7:

[0053] The server organizes sleep data and stores it in a secure database. The data from the current day is used to make suggestions for the following morning.

[0054] Step 8:

[0055] When the user wakes up, the device generates and displays personalized activity improvement suggestions based on sleep data and the day's schedule. These suggestions may include specific action plans.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] Conventional sleep support systems fail to effectively utilize users' biometric and environmental information to provide an optimal sleep environment for each individual user. Furthermore, they struggle to quickly detect abnormal health conditions and take appropriate measures.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for acquiring biological and external information in real time, means for analyzing the acquired information and determining the sleep state, and means for adjusting physical conditions according to the determined state. This makes it possible to provide the user with an optimal sleep environment and to quickly detect and respond to abnormalities in their health.

[0061] "Biometric information" is a general term for data related to an individual's physical state, such as brain activity, heart rate information, and respiratory information.

[0062] "External information" is a general term for data related to external conditions such as the indoor environment.

[0063] A "real-time data acquisition device" is a device that can measure and process data instantly.

[0064] A "device that analyzes and determines sleep state" is a device that analyzes acquired data to determine the user's current sleep state (e.g., REM sleep, non-REM sleep).

[0065] A "device for adjusting physical conditions" is a device for automatically optimizing the physical environment of bedding, such as its hardness, temperature, and angle.

[0066] A "health monitoring and abnormality detection device" is a device that monitors a user's biometric information and immediately detects any health-related abnormalities.

[0067] A "device that generates suggestions to improve activity efficiency" is a device that, based on sleep data, creates advice to help users effectively carry out their activities after waking up.

[0068] This invention is a system that acquires biometric and external information in real time during a user's sleep and provides an optimal sleep environment based on that information. The main components include a terminal composed of sensors for acquiring data, a server that analyzes the collected data and provides appropriate instructions, and a user interface for providing feedback to the user.

[0069] The device includes multiple sensors attached to the user's sleep environment. These sensors consist of an electroencephalogram (EEG) sensor to measure brain activity, a heart rate sensor to record heart rate information, a respiratory sensor to monitor breathing information, and room temperature and light sensors to acquire external information. This information is collected in real time and transmitted to a server using wireless technology.

[0070] The server receives the collected data and performs analysis using a generative AI model. This analysis allows the server to determine the user's sleep state (REM sleep, non-REM sleep, etc.). It then calculates the physical conditions necessary to provide the optimal sleep environment. These calculations include parameters for automatically adjusting the bed's hardness, temperature, and angle.

[0071] Furthermore, the server continuously monitors biometric information to detect anomalies, and if an abnormal condition is detected, it quickly sends an alert to the user. This alert is immediately sent to the user's smartphone or other device.

[0072] In addition, the device provides recommendations to improve the user's activity level each morning before they wake up. These recommendations are based on the previous night's sleep data, as well as the day's schedule and expected activities.

[0073] For example, if a user's heart rate abnormally increases during normal sleep, the server detects this information and sends an alert to the user. Simultaneously, the device uses an environmental adjustment device to change the hardness and temperature of the bed, transitioning the user from deep sleep to light sleep. This series of actions allows the user to continue sleeping with peace of mind.

[0074] An example of a prompt can be the text "Generate an appropriate alert message when an abnormal heart rate is detected." This prompt will cause the generation AI model to create an appropriate response.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The terminal acquires biometric and external information using sensors attached to the user's bed. Specific inputs include electroencephalogram (EEG), heart rate, respiratory pattern, room temperature, and illuminance. An integrated circuit and data conversion unit within the terminal convert the analog signals from these sensors into digital data. The resulting output is real-time digital data, transmitted to a server via wireless communication technology.

[0078] Step 2:

[0079] The server receives digital data transmitted from the terminal. The input data consists of real-time biometric and environmental data. The server inputs this data into a generating AI model to analyze sleep states. Data processing includes statistical methods and time series analysis, and the output determines the user's sleep stage (e.g., REM sleep or non-REM sleep).

[0080] Step 3:

[0081] The server calculates environmental adjustment parameters based on the determined sleep stage. The input is the sleep stage obtained in the previous step. The output is instructions for adjusting the bed's hardness, temperature, and angle. The server utilizes machine learning algorithms to perform comparative analysis with past data and derive the optimal environmental parameters.

[0082] Step 4:

[0083] The terminal executes environmental adjustment instructions received from the server. The input consists of environmental adjustment parameters from the server. The terminal operates its built-in motor and temperature control unit to adjust the bed's hardness and temperature. The output is a sleep environment optimized for the user's current sleep stage.

[0084] Step 5:

[0085] The server continuously monitors the user's biometric data while they sleep. The input is real-time biometric data. The server runs an anomaly detection algorithm to identify abnormal values, and if there is a health risk, it generates an alert as output. This alert is immediately sent to the user's smart device.

[0086] Step 6:

[0087] The device prepares advice for the user each morning before they wake up, helping them improve their activity efficiency. Inputs include sleep data from the previous night and information about their schedule for the day. Using data analysis tools, it generates helpful suggestions for the user's daily activities. The output is advice provided to the user, enabling them to have a healthy and efficient day.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In modern society, many people suffer from poor sleep quality due to irregular lifestyles and stress. This leads to health problems and decreased productivity. A particular challenge is the lack of a system that optimizes sleep environments according to individual lifestyles and proposes healthy lifestyle habits.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes a sensor means for acquiring biological data during sleep in real time, an analysis means for analyzing the acquired biological data and determining the sleep stage, an environment adjustment means for adjusting the physical environment according to the determined sleep stage, and a recommendation generation means for generating recommendations to optimize lifestyle habits based on lifestyle data. This makes it possible to individually optimize the user's sleep environment and lifestyle habits, thereby improving their health and quality of life.

[0093] A "sensor" is a device that acquires biological data and external environmental data in real time during sleep.

[0094] "Analysis means" refers to a system that analyzes acquired biometric data and has the function of determining the user's sleep stage.

[0095] An "environmental adjustment device" is a device equipped with a function to adjust the physical environment according to the determined sleep stage.

[0096] A "health monitoring system" is a means of monitoring a user's health status based on sleep data and detecting abnormalities.

[0097] A "proposal generation means" is a system that has the function of generating suggestions to improve the efficiency of the day's activities upon waking up.

[0098] A "recommendation generation method" is a system that generates recommendations to optimize lifestyle habits based on lifestyle data.

[0099] The system implementing this invention collects and analyzes biometric data in real time during the user's sleep, thereby proposing an optimized sleep environment and lifestyle improvements for each individual user. The system has the following configuration.

[0100] The server uses machine learning algorithms to analyze biometric data transmitted from sensor devices and determine the user's sleep stage. This analysis includes data aggregation and filtering, as well as pattern recognition over time. Based on the analyzed data, the environmental adjustment system adjusts the bed's firmness, temperature, and tilt to provide an optimal sleep environment. This ensures that the optimal environment is tailored to each sleep stage.

[0101] Familiar smartphones and smartwatches function as sensors, monitoring brain waves, heart rate, breathing patterns, and the external environment in real time. This allows the server to detect changes in the user's health status and generate alerts through health monitoring devices if abnormalities are detected.

[0102] Furthermore, the server, through its suggestion generation mechanism, provides specific feedback based on the user's sleep data to maximize their activity efficiency the following day. These suggestions contribute to improving the user's daily life.

[0103] For example, if fatigue is detected in a user's sleep data for a given day, the server will notify the user that relaxation is needed in their schedule for the next day, thereby encouraging healthy lifestyle habits. A generative AI model is used to generate these instructions, employing a prompt such as: "Generate relaxing daily activity suggestions based on the user's sleep data."

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The device uses a smartwatch or smartphone to acquire data on the user's brainwaves, heart rate, breathing patterns, and external environmental data such as room temperature and illuminance. This data is collected in real time through sensors. Biometric data is collected as input and sent to a server.

[0107] Step 2:

[0108] The server analyzes the received biometric data. Using machine learning algorithms, it determines the user's sleep stage from the biometric data. The input is the biometric data collected in step 1, and the output here is the determination of the current sleep stage. Data processing includes time-series analysis and clustering.

[0109] Step 3:

[0110] The server calculates the optimal environmental adjustment parameters based on the determined sleep stage. The input is information about the sleep stage, and the output is the environmental adjustment parameters. The generated parameters are sent to the terminal, which automatically adjusts the firmness, temperature, and tilt of the bed.

[0111] Step 4:

[0112] The server continues to monitor the data and, as a health monitoring tool, immediately generates an alert if an abnormal pattern is detected. The input is continuously collected biometric data, and the output is an alert indicating an anomaly, such as a sudden increase in heart rate.

[0113] Step 5:

[0114] After the user has finished sleeping, the server uses the sleep data to generate specific lifestyle improvement suggestions using a suggestion generation mechanism. The input data is the user's sleep history, and the output is suggestions for lifestyle improvement. In this process, a generation AI model is used to analyze prompt sentences and provide recommendations regarding daily activities for the following day.

[0115] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0116] This invention is a system for comprehensively analyzing a user's biometric data and emotional state during sleep to provide an optimal sleep environment. This system, by combining multiple means as described below, provides the user with high-quality sleep and improves their activity efficiency after waking.

[0117] First, the device uses a group of sensors installed on the bed to acquire data on the user's brainwaves, heart rate, breathing patterns, and external environmental data such as room temperature and illuminance. This data is transmitted to the server in real time. An emotion engine is also built in, which analyzes the user's emotional state from their voice and facial expressions. Emotional data is transmitted along with biometric data.

[0118] The server uses machine learning algorithms to analyze the received biometric and emotional data. This determines the user's sleep stage and then decides on the optimal environment, taking their emotional state into consideration. Based on these results, the server calculates environmental adjustment parameters and sends instructions to the terminal.

[0119] Next, the device adjusts the physical characteristics of the bed (firmness, temperature, and tilt) according to the settings sent from the server. This provides an optimal environment tailored to the user's emotions and sleep stage. For example, if the user is sleeping under stress, the environment is fine-tuned to enhance relaxation.

[0120] In addition, the server continuously monitors sleep data and, if any abnormalities are detected, generates an alert to warn of health risks. This alert is automatically sent to the user's smartphone, prompting them to consult a medical professional.

[0121] Furthermore, each morning, the device provides personalized advice to maximize activity efficiency based on the user's sleep data, emotional data, and daily schedule. For example, if the user's emotional state the previous night was stressful, it will recommend exercise to reduce stress. This allows the user to have a healthy and efficient day.

[0122] For example, if a user feels anxious when falling asleep, the emotion engine detects this, and the server adjusts the environment to promote relaxation. At this time, the device adjusts the bed to be slightly softer and lowers the room temperature to a comfortable level. As a result, the user can enter a calmer state and achieve higher quality sleep. Thus, the present invention is a system that comprehensively utilizes the user's biometric and emotional data to optimize the sleep environment and activity efficiency.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] The device uses a group of sensors installed on the bed to acquire biometric data such as brain waves, heart rate, breathing patterns, temperature, and illuminance, as well as environmental data, in real time. In addition, it uses a built-in emotion engine to analyze the user's voice and facial expressions to acquire emotional data.

[0126] Step 2:

[0127] The device transmits acquired biometric and emotional data to the server. The data is transferred in real time and serves as foundational data for analysis.

[0128] Step 3:

[0129] The server analyzes the received data in real time and uses machine learning algorithms to determine the user's sleep stage. It also analyzes the user's current emotional state based on emotional data.

[0130] Step 4:

[0131] The server calculates the optimal sleep environment based on the analysis results. It takes into account sleep stages and emotional states and generates adjustment parameters for bed firmness, temperature, and tilt.

[0132] Step 5:

[0133] The device receives adjustment parameters sent from the server and automatically adjusts the physical properties of the bed. This provides an environment suited to the user's sleep stage and emotional state.

[0134] Step 6:

[0135] The server continuously monitors sleep data and immediately issues an alert if it detects any abnormalities, such as a sudden increase in heart rate. The alert is displayed on the user's mobile device or bedside display.

[0136] Step 7:

[0137] The server organizes the data from the night and saves it to a historical database. The sleep and emotional data from that day are used to form suggestions the following morning.

[0138] Step 8:

[0139] When the user wakes up, the device generates and displays suggestions for improving activity efficiency based on the previous night's sleep and emotional state. These suggestions may include specific action plans, such as stress reduction strategies and recommended break times.

[0140] (Example 2)

[0141] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0142] Conventional sleep management systems are limited to acquiring biometric information and simple environmental settings, and are insufficient in providing an optimal sleep environment that takes into account the user's emotional state. Furthermore, they have limitations in detecting abnormalities and providing individualized instructions after waking up. It is necessary to address these challenges and achieve higher quality sleep and improved activity efficiency after waking.

[0143] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0144] In this invention, the server includes information acquisition means for acquiring biometric and environmental information in real time, information analysis means for determining sleep stages and analyzing emotional states using the acquired information, and adjustment means for dynamically adjusting the physical environment based on the analyzed sleep stages and emotional states. This makes it possible to provide an optimal sleep environment according to the user's emotional state, detect anomalies, and improve the efficiency of activities after waking up.

[0145] "Biometric information" refers to data obtained from the user's body, including, for example, brain waves, heart rate, and breathing patterns.

[0146] "Environmental information" refers to data about the physical conditions surrounding the user, including, for example, temperature, humidity, and illuminance.

[0147] "Information acquisition means" refers to devices and mechanisms for collecting biological and environmental information in real time.

[0148] "Information analysis means" refers to analysis devices and algorithms that use acquired biometric and environmental information to determine the user's sleep stage and emotional state.

[0149] "Adjustment means" refers to devices or systems that dynamically change the physical environment based on the analysis results.

[0150] "Health monitoring means" refers to a system that continuously monitors data during sleep and detects abnormalities.

[0151] "Instruction generation means" refers to devices or technologies for generating instructions to improve activity efficiency upon waking.

[0152] The system based on this invention collects and analyzes biometric and environmental information to provide users with an optimal sleep environment in order to achieve high-quality sleep. This system mainly consists of terminals and a server.

[0153] The device acquires the user's biometric information in real time using various sensors installed on the bed. Specific examples of sensors used include electroencephalogram (EEG), heart rate, and respiratory pattern sensors. External environmental information is also acquired using temperature, humidity, and light sensors. Some parts of the device incorporate an emotion engine that analyzes voice data and facial images to evaluate the user's emotional state. The acquired data is then transmitted directly to the server.

[0154] The server analyzes the received biometric and environmental information using machine learning algorithms. Specifically, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used to determine the user's sleep stage and emotional state. Based on this analysis, the server calculates the optimal environmental settings and sends instructions to the terminal.

[0155] For example, if a user falls asleep feeling stressed, the emotion engine detects this state, and the server adjusts settings to enhance relaxation. The device then adjusts the bed's firmness and controls the room temperature to an appropriate level. This allows the user to fall into a smoother, higher-quality sleep.

[0156] Furthermore, this system can monitor sleep abnormalities and, if detected, send alerts to the user via a smartphone or other device. Upon waking, advice is provided using a generative AI model based on the previous night's sleep data and emotional state, to maximize the day's activity efficiency. An example of a prompt for the generative AI model is, "If the user's emotional state the previous day was anxious, what advice should you provide?"

[0157] In this way, the system actively supports the user's health and helps them achieve a better life.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The terminal acquires biometric and environmental information in real time from various sensors installed on the bed. Inputs include data such as brain waves, heart rate, and respiratory patterns, to which temperature, humidity, and illuminance data are added. This information is converted into digital signals and the acquired data is immediately transmitted to the server.

[0161] Step 2:

[0162] The server receives biometric and emotional information transmitted from the terminal. The data received as input is analyzed using machine learning algorithms, particularly CNNs and RNNs. Here, the data is processed to determine the user's sleep stage and emotional state, and the analysis results containing this information are obtained as output. Specifically, this involves a process of analyzing the data while adjusting the model parameters.

[0163] Step 3:

[0164] The server determines the physical environment settings based on the analysis results and calculates environmental parameters to send to the terminal. This step considers the analyzed sleep stages and emotional states as input. The resulting calculated environmental settings (temperature, hardness, tilt, etc.) are output and sent to the terminal. The process involves generating control commands to provide the optimal environment.

[0165] Step 4:

[0166] The terminal adjusts the physical properties of the bed based on environment setting parameters received from the server. Inputs include control commands sent from the server, and outputs include actual environmental changes (e.g., adjustment of bed hardness, temperature changes). Specific operations include using devices such as linear actuators to perform physical adjustments.

[0167] Step 5:

[0168] The server continuously monitors the user's sleep data and generates alerts if anomalies are detected. It receives continuously collected sleep data as input and generates warning messages as output by detecting anomalies. Its operation includes real-time data monitoring and the execution of comparison algorithms.

[0169] Step 6:

[0170] Every morning, the device considers the user's sleep and emotional data and uses a generative AI model to provide advice to maximize activity efficiency. Past sleep data and daily schedules are considered as input, and personalized suggestions are generated as output. Specific operations include selecting suggestions and notifying the user.

[0171] (Application Example 2)

[0172] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0173] In modern society, poor sleep quality negatively impacts health and activity efficiency. To address this problem, it is necessary to comprehensively analyze biometric data and emotional states during sleep and provide an optimal sleep environment. Conventional systems focus on determining sleep stages, but do not adequately adjust the environment considering emotional states. Furthermore, there is a lack of anomaly detection to prevent health risks and suggestions to improve activity efficiency. Therefore, a system that can comprehensively manage these aspects is needed.

[0174] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0175] In this invention, the server includes adjustment means for determining emotional state and optimizing environmental settings, environment modification means for adjusting the physical environment according to the determined sleep stage and emotional state, and health management means for monitoring health status based on sleep information and detecting abnormalities. This makes it possible to provide an optimal sleep environment tailored to the individual needs of the user, enable early detection of health risks, and offer suggestions to support efficient activities.

[0176] A "sensing device" is a device that collects biometric information such as brain waves, heart rate, and breathing patterns from the user in real time while they are sleeping, and also acquires information about the external environment.

[0177] The "analysis means" refers to the function responsible for the process of determining the user's sleep stage based on acquired biometric information.

[0178] "Adjustment mechanisms" refer to functions that evaluate emotional states and optimize environmental settings based on that information.

[0179] The "environmental modification means" is a device that automatically adjusts the hardness, temperature, and angle of the lying surface according to the determined sleep stage and emotional state.

[0180] A "health management system" is a mechanism for continuously monitoring health status based on sleep information and taking appropriate action when an abnormality is detected.

[0181] The "proposal creation method" is a function that generates and provides specific suggestions to maximize the efficiency of the day's activities when the user wakes up.

[0182] The system for realizing this application primarily consists of sensing means, analysis means, adjustment means, environment modification means, health management means, and proposal generation means. Each means is implemented specifically as follows.

[0183] The server uses machine learning algorithms such as Python's Scikit-learn to analyze biometric information and external environmental information received from sensing devices. This allows it to determine the user's sleep stage and further evaluate their emotional state based on data such as their voice and facial expressions.

[0184] Based on the information it has assessed, the terminal automatically determines environmental settings tailored to the user's emotional state via an adjustment mechanism. This data is transmitted from a server, and the terminal uses an environmental modification mechanism to adjust the hardness, temperature, and angle of the sleeping surface, thereby creating an optimal sleep environment. The necessary hardware for this includes a bed with a variable structure and room temperature control equipment.

[0185] Furthermore, the server will continue to monitor the user's health status during sleep using health management tools, and will notify the user's smartphone if any abnormalities are detected. This notification will prompt the user to consult a medical professional if necessary.

[0186] Furthermore, upon waking, users are provided with specific suggestions for improving their activity efficiency, generated by the suggestion generation system. This includes, for example, recommendations for exercise or relaxation if the user experienced high levels of stress the previous day.

[0187] For example, if anxiety is detected before sleep, the server will change settings to promote relaxation, the device will adjust the bed firmness, and the room temperature will be set to a comfortable level. Through this process, the user can get higher quality sleep.

[0188] An example of a prompt message is: "Based on the user's sleep data, please provide the optimal environment. In particular, please tell me how to handle situations where the user is feeling anxious."

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The server receives the user's brainwaves, heart rate, breathing pattern, and external environment information transmitted from the sensing device as input data. Based on this biometric information, it uses a machine learning model with Scikit-learn to determine the sleep stage. The current sleep stage is generated as output.

[0192] Step 2:

[0193] The server applies an emotion recognition model to analyze the user's voice and facial image data to determine their emotional state. This analysis treats the emotional state (e.g., stress, anxiety, relaxation) as input data, and the emotional state is obtained as output.

[0194] Step 3:

[0195] The server combines the sleep stages and emotional states obtained in Steps 1 and 2 to determine appropriate environmental settings. Using prompts from the generative AI model, it calculates the specific setting parameters to be set by the environmental modification device and sends them to the terminal. The output is instruction data for environmental adjustment.

[0196] Step 4:

[0197] Based on instruction data received from the server, the terminal uses environmental modification mechanisms to automatically adjust the hardness of the sleeping surface, the room temperature, and the angle. This makes specific physical changes to provide an optimal sleeping environment.

[0198] Step 5:

[0199] The server continuously monitors biometric information during sleep using health management tools. If an abnormality is detected, it generates an alert and sends a notification about the health risk to the user's smartphone. The output is the notification information for the abnormality.

[0200] Step 6:

[0201] When a user wakes up, the server provides their device or smartphone with suggestions to improve their activity efficiency, generated through a suggestion generation system. These suggestions include specific actions (e.g., exercise, relaxation activities) based on the previous day's sleep and emotional data. The output is the content of these suggestions.

[0202] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0203] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0204] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0208] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0209] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0210] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0211] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0212] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0213] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0214] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0215] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0216] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0217] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0218] This invention is a system for acquiring biometric data in real time during a user's sleep and providing an optimal sleep environment based on that data. This system consists of several key components that work together to provide the user with high-quality sleep and improve their activity efficiency after waking up.

[0219] First, the device acquires data on the user's brainwaves, heart rate, breathing patterns, and external environmental data such as room temperature and illuminance through a group of sensors installed on the bed. This data is transmitted to the server in real time.

[0220] The server utilizes machine learning algorithms to analyze the received biometric data. These algorithms accurately determine the user's sleep stage and provide a basis for determining the most suitable sleep environment. Based on these results, the server calculates environmental adjustment parameters and sends instructions back to the terminal.

[0221] Next, the device adjusts the physical characteristics of the bed (firmness, temperature, and tilt) according to instructions from the server. This allows the user to rest in an optimal environment for each stage of sleep. Because this adjustment is made flexibly according to the depth of sleep, the user can enjoy higher quality sleep.

[0222] In addition, the server continuously monitors sleep data and generates alerts to warn of health risks if abnormal data patterns are detected. These alerts are automatically sent to the user's smartphone, prompting them to consult a medical professional if necessary.

[0223] Furthermore, the device reviews the user's sleep data and daily schedule each morning to provide personalized advice to maximize activity efficiency. This advice goes beyond mere recommendations, serving as a guide for the user to have a healthy and productive day.

[0224] As a concrete example, suppose a user experiences a sudden increase in heart rate during normal sleep. In this case, the server immediately detects this anomaly and sends an alert to the user. Simultaneously, the device adjusts the firmness and temperature of the bed to help the user transition from deep sleep to light sleep. The following morning, the user is provided with advice on how to improve their activity efficiency, taking this anomaly into account. Thus, the present invention is a system that comprehensively supports the quality of a user's sleep.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The device acquires brain waves, heart rate, breathing patterns, and room temperature and illuminance from sleep sensors. This data is collected in real time at regular intervals.

[0228] Step 2:

[0229] The device transmits the acquired biometric data to the server. The data is encrypted and sent through a secure communication channel.

[0230] Step 3:

[0231] The server processes the received data through an analysis algorithm to determine the sleep stage (light sleep, deep sleep, REM sleep). This analysis also takes into account past sleep data to derive the optimal analysis result.

[0232] Step 4:

[0233] The server calculates the optimal environment settings based on the analysis results. These settings include bed hardness, temperature, and incline.

[0234] Step 5:

[0235] The terminal receives the environment settings sent from the server and adjusts the bed's physical mechanisms. Specifically, it activates the built-in motors and temperature control devices to apply the settings.

[0236] Step 6:

[0237] The server continuously monitors the data and sends an alert to the user if it detects an abnormal data pattern that exceeds a specified range (e.g., a sudden increase in heart rate). The alert is displayed on a smartphone or bedside display.

[0238] Step 7:

[0239] The server organizes sleep data and stores it in a secure database. The data from the current day is used to make suggestions for the following morning.

[0240] Step 8:

[0241] When the user wakes up, the device generates and displays personalized activity improvement suggestions based on sleep data and the day's schedule. These suggestions may include specific action plans.

[0242] (Example 1)

[0243] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0244] Conventional sleep support systems fail to effectively utilize users' biometric and environmental information to provide an optimal sleep environment for each individual user. Furthermore, they struggle to quickly detect abnormal health conditions and take appropriate measures.

[0245] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0246] In this invention, the server includes means for acquiring biological and external information in real time, means for analyzing the acquired information and determining the sleep state, and means for adjusting physical conditions according to the determined state. This makes it possible to provide the user with an optimal sleep environment and to quickly detect and respond to abnormalities in their health.

[0247] "Biometric information" is a general term for data related to an individual's physical state, such as brain activity, heart rate information, and respiratory information.

[0248] "External information" is a general term for data related to external conditions such as the indoor environment.

[0249] A "real-time data acquisition device" is a device that can measure and process data instantly.

[0250] A "device that analyzes and determines sleep state" is a device that analyzes acquired data to determine the user's current sleep state (e.g., REM sleep, non-REM sleep).

[0251] A "device for adjusting physical conditions" is a device for automatically optimizing the physical environment of bedding, such as its hardness, temperature, and angle.

[0252] A "health monitoring and abnormality detection device" is a device that monitors a user's biometric information and immediately detects any health-related abnormalities.

[0253] A "device that generates suggestions to improve activity efficiency" is a device that, based on sleep data, creates advice to help users effectively carry out their activities after waking up.

[0254] This invention is a system that acquires biometric and external information in real time during a user's sleep and provides an optimal sleep environment based on that information. The main components include a terminal composed of sensors for acquiring data, a server that analyzes the collected data and provides appropriate instructions, and a user interface for providing feedback to the user.

[0255] The device includes multiple sensors attached to the user's sleep environment. These sensors consist of an electroencephalogram (EEG) sensor to measure brain activity, a heart rate sensor to record heart rate information, a respiratory sensor to monitor breathing information, and room temperature and light sensors to acquire external information. This information is collected in real time and transmitted to a server using wireless technology.

[0256] The server receives the collected data and performs analysis using a generative AI model. This analysis allows the server to determine the user's sleep state (REM sleep, non-REM sleep, etc.). It then calculates the physical conditions necessary to provide the optimal sleep environment. These calculations include parameters for automatically adjusting the bed's hardness, temperature, and angle.

[0257] Furthermore, the server continuously monitors biometric information to detect anomalies, and if an abnormal condition is detected, it quickly sends an alert to the user. This alert is immediately sent to the user's smartphone or other device.

[0258] In addition, the device provides recommendations to improve the user's activity level each morning before they wake up. These recommendations are based on the previous night's sleep data, as well as the day's schedule and expected activities.

[0259] For example, if a user's heart rate abnormally increases during normal sleep, the server detects this information and sends an alert to the user. Simultaneously, the device uses an environmental adjustment device to change the hardness and temperature of the bed, transitioning the user from deep sleep to light sleep. This series of actions allows the user to continue sleeping with peace of mind.

[0260] An example of a prompt can be the text "Generate an appropriate alert message when an abnormal heart rate is detected." This prompt will cause the generation AI model to create an appropriate response.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The terminal acquires biometric and external information using sensors attached to the user's bed. Specific inputs include electroencephalogram (EEG), heart rate, respiratory pattern, room temperature, and illuminance. An integrated circuit and data conversion unit within the terminal convert the analog signals from these sensors into digital data. The resulting output is real-time digital data, transmitted to a server via wireless communication technology.

[0264] Step 2:

[0265] The server receives digital data transmitted from the terminal. The input data consists of real-time biometric and environmental data. The server inputs this data into a generating AI model to analyze sleep states. Data processing includes statistical methods and time series analysis, and the output determines the user's sleep stage (e.g., REM sleep or non-REM sleep).

[0266] Step 3:

[0267] The server calculates environmental adjustment parameters based on the determined sleep stage. The input is the sleep stage obtained in the previous step. The output is instructions for adjusting the bed's hardness, temperature, and angle. The server utilizes machine learning algorithms to perform comparative analysis with past data and derive the optimal environmental parameters.

[0268] Step 4:

[0269] The terminal executes environmental adjustment instructions received from the server. The input consists of environmental adjustment parameters from the server. The terminal operates its built-in motor and temperature control unit to adjust the bed's hardness and temperature. The output is a sleep environment optimized for the user's current sleep stage.

[0270] Step 5:

[0271] The server continuously monitors the user's biometric data while they sleep. The input is real-time biometric data. The server runs an anomaly detection algorithm to identify abnormal values, and if there is a health risk, it generates an alert as output. This alert is immediately sent to the user's smart device.

[0272] Step 6:

[0273] The device prepares advice for the user each morning before they wake up, helping them improve their activity efficiency. Inputs include sleep data from the previous night and information about their schedule for the day. Using data analysis tools, it generates helpful suggestions for the user's daily activities. The output is advice provided to the user, enabling them to have a healthy and efficient day.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] In modern society, many people suffer from poor sleep quality due to irregular lifestyles and stress. This leads to health problems and decreased productivity. A particular challenge is the lack of a system that optimizes sleep environments according to individual lifestyles and proposes healthy lifestyle habits.

[0277] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0278] In this invention, the server includes a sensor means for acquiring biological data during sleep in real time, an analysis means for analyzing the acquired biological data and determining the sleep stage, an environment adjustment means for adjusting the physical environment according to the determined sleep stage, and a recommendation generation means for generating recommendations to optimize lifestyle habits based on lifestyle data. This makes it possible to individually optimize the user's sleep environment and lifestyle habits, thereby improving their health and quality of life.

[0279] A "sensor" is a device that acquires biological data and external environmental data in real time during sleep.

[0280] "Analysis means" refers to a system that analyzes acquired biometric data and has the function of determining the user's sleep stage.

[0281] The "environmental adjustment means" is a device equipped with a function to adjust the physical environment according to the determined sleep stage.

[0282] The "health monitoring means" is a means to monitor the user's health status based on sleep data and detect abnormalities.

[0283] The "proposal generation means" is a system having a function to generate proposals for improving the activity efficiency of the day at the time of waking up.

[0284] The "recommendation generation means" is a system that generates recommendations for optimizing lifestyle habits based on lifestyle data.

[0285] The system for implementing this invention collects and analyzes biometric data in real time during the user's sleep, and proposes improvements to the sleep environment and lifestyle habits optimized for each individual user. The system has the following configuration.

[0286] The server utilizes a machine learning algorithm to analyze the biometric data transmitted from the sensor means and determines the user's sleep stage. This analysis includes data aggregation and filtering, and pattern recognition over time. Based on the analyzed data, the environmental adjustment means adjusts the hardness, temperature, and inclination of the bed to provide an optimal sleep environment. Thereby, an optimal environment corresponding to each sleep stage is realized.

[0287] A familiar smartphone or smartwatch functions as the sensor means and monitors brain waves, heart rate, breathing pattern, and the external environment in real time. Thereby, the server detects changes in the user's health status and generates an alert through the health monitoring means if there is an abnormality.

[0288] Furthermore, the server provides specific feedback through the proposal generation means to maximize the activity efficiency of the next day based on data related to the user's sleep. These proposals lead to improvements in the user's daily life.

[0289] For example, if fatigue is detected in a user's sleep data for a given day, the server will notify the user that relaxation is needed in their schedule for the next day, thereby encouraging healthy lifestyle habits. A generative AI model is used to generate these instructions, employing a prompt such as: "Generate relaxing daily activity suggestions based on the user's sleep data."

[0290] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0291] Step 1:

[0292] The device uses a smartwatch or smartphone to acquire data on the user's brainwaves, heart rate, breathing patterns, and external environmental data such as room temperature and illuminance. This data is collected in real time through sensors. Biometric data is collected as input and sent to a server.

[0293] Step 2:

[0294] The server analyzes the received biometric data. Using machine learning algorithms, it determines the user's sleep stage from the biometric data. The input is the biometric data collected in step 1, and the output here is the determination of the current sleep stage. Data processing includes time-series analysis and clustering.

[0295] Step 3:

[0296] The server calculates the optimal environmental adjustment parameters based on the determined sleep stage. The input is information about the sleep stage, and the output is the environmental adjustment parameters. The generated parameters are sent to the terminal, which automatically adjusts the firmness, temperature, and tilt of the bed.

[0297] Step 4:

[0298] The server continues to monitor the data and, as a health monitoring tool, immediately generates an alert if an abnormal pattern is detected. The input is continuously collected biometric data, and the output is an alert indicating an anomaly, such as a sudden increase in heart rate.

[0299] Step 5:

[0300] After the user has finished sleeping, the server uses the sleep data to generate specific lifestyle improvement suggestions using a suggestion generation mechanism. The input data is the user's sleep history, and the output is suggestions for lifestyle improvement. In this process, a generation AI model is used to analyze prompt sentences and provide recommendations regarding daily activities for the following day.

[0301] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0302] This invention is a system for comprehensively analyzing a user's biometric data and emotional state during sleep to provide an optimal sleep environment. This system, by combining multiple means as described below, provides the user with high-quality sleep and improves their activity efficiency after waking.

[0303] First, the device uses a group of sensors installed on the bed to acquire data on the user's brainwaves, heart rate, breathing patterns, and external environmental data such as room temperature and illuminance. This data is transmitted to the server in real time. An emotion engine is also built in, which analyzes the user's emotional state from their voice and facial expressions. Emotional data is transmitted along with biometric data.

[0304] The server uses machine learning algorithms to analyze the received biological and emotional data. Thereby, it determines the user's sleep stage and further decides on the optimal environment considering the emotional state. Based on this result, the server calculates environmental adjustment parameters and sends instructions to the terminal.

[0305] Next, the terminal adjusts the physical characteristics (firmness, temperature, inclination) of the bed according to the settings sent from the server. Thereby, it provides an optimal environment according to the user's emotions and sleep stage. For example, when sleeping in a stressed state, the environment is finely adjusted to enhance the relaxation effect.

[0306] In addition, the server continuously monitors the data during sleep and generates an alert to warn of health risks if an abnormality is detected. This alert is automatically sent to the user's smartphone to prompt consultation with a medical expert.

[0307] Furthermore, every morning, the terminal provides personalized advice to maximize activity efficiency based on the user's sleep data, emotional data, and the schedule for the day. For example, if the emotional state the previous night was stress, it recommends exercise to reduce stress. Thereby, the user can have a healthy and efficient day.

[0308] As a specific example, when the user feels不安 while lying in bed, the emotion engine detects this, and the server makes environmental settings to promote the relaxation effect. At this time, the terminal adjusts the bed to be a little softer and lowers the indoor temperature to a comfortable level. As a result, the user can fall asleep in a calm state and have higher-quality sleep. Thus, the present invention is a system that comprehensively utilizes the user's biological data and emotional data to optimize the sleep environment and activity efficiency.

[0309] The processing flow will be described below.

[0310] Step 1:

[0311] The device uses a group of sensors installed on the bed to acquire biometric data such as brain waves, heart rate, breathing patterns, temperature, and illuminance, as well as environmental data, in real time. In addition, it uses a built-in emotion engine to analyze the user's voice and facial expressions to acquire emotional data.

[0312] Step 2:

[0313] The device transmits acquired biometric and emotional data to the server. The data is transferred in real time and serves as foundational data for analysis.

[0314] Step 3:

[0315] The server analyzes the received data in real time and uses machine learning algorithms to determine the user's sleep stage. It also analyzes the user's current emotional state based on emotional data.

[0316] Step 4:

[0317] The server calculates the optimal sleep environment based on the analysis results. It takes into account sleep stages and emotional states and generates adjustment parameters for bed firmness, temperature, and tilt.

[0318] Step 5:

[0319] The device receives adjustment parameters sent from the server and automatically adjusts the physical properties of the bed. This provides an environment suited to the user's sleep stage and emotional state.

[0320] Step 6:

[0321] The server continuously monitors sleep data and immediately issues an alert if it detects any abnormalities, such as a sudden increase in heart rate. The alert is displayed on the user's mobile device or bedside display.

[0322] Step 7:

[0323] The server organizes the data from the night and saves it to a historical database. The sleep and emotional data from that day are used to form suggestions the following morning.

[0324] Step 8:

[0325] When the user wakes up, the device generates and displays suggestions for improving activity efficiency based on the previous night's sleep and emotional state. These suggestions may include specific action plans, such as stress reduction strategies and recommended break times.

[0326] (Example 2)

[0327] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0328] Conventional sleep management systems are limited to acquiring biometric information and simple environmental settings, and are insufficient in providing an optimal sleep environment that takes into account the user's emotional state. Furthermore, they have limitations in detecting abnormalities and providing individualized instructions after waking up. It is necessary to address these challenges and achieve higher quality sleep and improved activity efficiency after waking.

[0329] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0330] In this invention, the server includes information acquisition means for acquiring biometric and environmental information in real time, information analysis means for determining sleep stages and analyzing emotional states using the acquired information, and adjustment means for dynamically adjusting the physical environment based on the analyzed sleep stages and emotional states. This makes it possible to provide an optimal sleep environment according to the user's emotional state, detect anomalies, and improve the efficiency of activities after waking up.

[0331] "Biometric information" refers to data obtained from the user's body, including, for example, brain waves, heart rate, and breathing patterns.

[0332] "Environmental information" refers to data about the physical conditions surrounding the user, including, for example, temperature, humidity, and illuminance.

[0333] "Information acquisition means" refers to devices and mechanisms for collecting biological and environmental information in real time.

[0334] "Information analysis means" refers to analysis devices and algorithms that use acquired biometric and environmental information to determine the user's sleep stage and emotional state.

[0335] "Adjustment means" refers to devices or systems that dynamically change the physical environment based on the analysis results.

[0336] "Health monitoring means" refers to a system that continuously monitors data during sleep and detects abnormalities.

[0337] "Instruction generation means" refers to devices or technologies for generating instructions to improve activity efficiency upon waking.

[0338] The system based on this invention collects and analyzes biometric and environmental information to provide users with an optimal sleep environment in order to achieve high-quality sleep. This system mainly consists of terminals and a server.

[0339] The device acquires the user's biometric information in real time using various sensors installed on the bed. Specific examples of sensors used include electroencephalogram (EEG), heart rate, and respiratory pattern sensors. External environmental information is also acquired using temperature, humidity, and light sensors. Some parts of the device incorporate an emotion engine that analyzes voice data and facial images to evaluate the user's emotional state. The acquired data is then transmitted directly to the server.

[0340] The server analyzes the received biometric and environmental information using machine learning algorithms. Specifically, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used to determine the user's sleep stage and emotional state. Based on this analysis, the server calculates the optimal environmental settings and sends instructions to the terminal.

[0341] For example, if a user falls asleep feeling stressed, the emotion engine detects this state, and the server adjusts settings to enhance relaxation. The device then adjusts the bed's firmness and controls the room temperature to an appropriate level. This allows the user to fall into a smoother, higher-quality sleep.

[0342] Furthermore, this system can monitor sleep abnormalities and, if detected, send alerts to the user via a smartphone or other device. Upon waking, advice is provided using a generative AI model based on the previous night's sleep data and emotional state, to maximize the day's activity efficiency. An example of a prompt for the generative AI model is, "If the user's emotional state the previous day was anxious, what advice should you provide?"

[0343] In this way, the system actively supports the user's health and helps them achieve a better life.

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1:

[0346] The terminal acquires biometric and environmental information in real time from various sensors installed on the bed. Inputs include data such as brain waves, heart rate, and respiratory patterns, to which temperature, humidity, and illuminance data are added. This information is converted into digital signals and the acquired data is immediately transmitted to the server.

[0347] Step 2:

[0348] The server receives biometric and emotional information transmitted from the terminal. The data received as input is analyzed using machine learning algorithms, particularly CNNs and RNNs. Here, the data is processed to determine the user's sleep stage and emotional state, and the analysis results containing this information are obtained as output. Specifically, this involves a process of analyzing the data while adjusting the model parameters.

[0349] Step 3:

[0350] The server determines the physical environment settings based on the analysis results and calculates environmental parameters to send to the terminal. This step considers the analyzed sleep stages and emotional states as input. The resulting calculated environmental settings (temperature, hardness, tilt, etc.) are output and sent to the terminal. The process involves generating control commands to provide the optimal environment.

[0351] Step 4:

[0352] The terminal adjusts the physical properties of the bed based on environment setting parameters received from the server. Inputs include control commands sent from the server, and outputs include actual environmental changes (e.g., adjustment of bed hardness, temperature changes). Specific operations include using devices such as linear actuators to perform physical adjustments.

[0353] Step 5:

[0354] The server continuously monitors the user's sleep data and generates alerts if anomalies are detected. It receives continuously collected sleep data as input and generates warning messages as output by detecting anomalies. Its operation includes real-time data monitoring and the execution of comparison algorithms.

[0355] Step 6:

[0356] Every morning, the device considers the user's sleep and emotional data and uses a generative AI model to provide advice to maximize activity efficiency. Past sleep data and daily schedules are considered as input, and personalized suggestions are generated as output. Specific operations include selecting suggestions and notifying the user.

[0357] (Application Example 2)

[0358] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0359] In modern society, poor sleep quality negatively impacts health and activity efficiency. To address this problem, it is necessary to comprehensively analyze biometric data and emotional states during sleep and provide an optimal sleep environment. Conventional systems focus on determining sleep stages, but do not adequately adjust the environment considering emotional states. Furthermore, there is a lack of anomaly detection to prevent health risks and suggestions to improve activity efficiency. Therefore, a system that can comprehensively manage these aspects is needed.

[0360] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0361] In this invention, the server includes adjustment means for determining emotional state and optimizing environmental settings, environment modification means for adjusting the physical environment according to the determined sleep stage and emotional state, and health management means for monitoring health status based on sleep information and detecting abnormalities. This makes it possible to provide an optimal sleep environment tailored to the individual needs of the user, enable early detection of health risks, and offer suggestions to support efficient activities.

[0362] A "sensing device" is a device that collects biometric information such as brain waves, heart rate, and breathing patterns from the user in real time while they are sleeping, and also acquires information about the external environment.

[0363] The "analysis means" refers to the function responsible for the process of determining the user's sleep stage based on acquired biometric information.

[0364] "Adjustment mechanisms" refer to functions that evaluate emotional states and optimize environmental settings based on that information.

[0365] The "environmental modification means" is a device that automatically adjusts the hardness, temperature, and angle of the lying surface according to the determined sleep stage and emotional state.

[0366] A "health management system" is a mechanism for continuously monitoring health status based on sleep information and taking appropriate action when an abnormality is detected.

[0367] The "proposal creation method" is a function that generates and provides specific suggestions to maximize the efficiency of the day's activities when the user wakes up.

[0368] The system for realizing this application primarily consists of sensing means, analysis means, adjustment means, environment modification means, health management means, and proposal generation means. Each means is implemented specifically as follows.

[0369] The server uses machine learning algorithms such as Python's Scikit-learn to analyze biometric information and external environmental information received from sensing devices. This allows it to determine the user's sleep stage and further evaluate their emotional state based on data such as their voice and facial expressions.

[0370] Based on the information it has assessed, the terminal automatically determines environmental settings tailored to the user's emotional state via an adjustment mechanism. This data is transmitted from a server, and the terminal uses an environmental modification mechanism to adjust the hardness, temperature, and angle of the sleeping surface, thereby creating an optimal sleep environment. The necessary hardware for this includes a bed with a variable structure and room temperature control equipment.

[0371] Furthermore, the server will continue to monitor the user's health status during sleep using health management tools, and will notify the user's smartphone if any abnormalities are detected. This notification will prompt the user to consult a medical professional if necessary.

[0372] Furthermore, upon waking, users are provided with specific suggestions for improving their activity efficiency, generated by the suggestion generation system. This includes, for example, recommendations for exercise or relaxation if the user experienced high levels of stress the previous day.

[0373] For example, if anxiety is detected before sleep, the server will change settings to promote relaxation, the device will adjust the bed firmness, and the room temperature will be set to a comfortable level. Through this process, the user can get higher quality sleep.

[0374] An example of a prompt message is: "Based on the user's sleep data, please provide the optimal environment. In particular, please tell me how to handle situations where the user is feeling anxious."

[0375] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0376] Step 1:

[0377] The server receives the user's brainwaves, heart rate, breathing pattern, and external environment information transmitted from the sensing device as input data. Based on this biometric information, it uses a machine learning model with Scikit-learn to determine the sleep stage. The current sleep stage is generated as output.

[0378] Step 2:

[0379] The server applies an emotion recognition model to analyze the user's voice and facial image data to determine their emotional state. This analysis treats the emotional state (e.g., stress, anxiety, relaxation) as input data, and the emotional state is obtained as output.

[0380] Step 3:

[0381] The server combines the sleep stages and emotional states obtained in Steps 1 and 2 to determine appropriate environmental settings. Using prompts from the generative AI model, it calculates the specific setting parameters to be set by the environmental modification device and sends them to the terminal. The output is instruction data for environmental adjustment.

[0382] Step 4:

[0383] Based on instruction data received from the server, the terminal uses environmental modification mechanisms to automatically adjust the hardness of the sleeping surface, the room temperature, and the angle. This makes specific physical changes to provide an optimal sleeping environment.

[0384] Step 5:

[0385] The server continuously monitors biometric information during sleep using health management tools. If an abnormality is detected, it generates an alert and sends a notification about the health risk to the user's smartphone. The output is the notification information for the abnormality.

[0386] Step 6:

[0387] When a user wakes up, the server provides their device or smartphone with suggestions to improve their activity efficiency, generated through a suggestion generation system. These suggestions include specific actions (e.g., exercise, relaxation activities) based on the previous day's sleep and emotional data. The output is the content of these suggestions.

[0388] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0389] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0390] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0391] [Third Embodiment]

[0392] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0393] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0394] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0395] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0396] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0397] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0398] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0399] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0400] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0401] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0402] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0403] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0404] This invention is a system for acquiring biometric data in real time during a user's sleep and providing an optimal sleep environment based on that data. This system consists of several key components that work together to provide the user with high-quality sleep and improve their activity efficiency after waking up.

[0405] First, the device acquires data on the user's brainwaves, heart rate, breathing patterns, and external environmental data such as room temperature and illuminance through a group of sensors installed on the bed. This data is transmitted to the server in real time.

[0406] The server utilizes machine learning algorithms to analyze the received biometric data. These algorithms accurately determine the user's sleep stage and provide a basis for determining the most suitable sleep environment. Based on these results, the server calculates environmental adjustment parameters and sends instructions back to the terminal.

[0407] Next, the device adjusts the physical characteristics of the bed (firmness, temperature, and tilt) according to instructions from the server. This allows the user to rest in an optimal environment for each stage of sleep. Because this adjustment is made flexibly according to the depth of sleep, the user can enjoy higher quality sleep.

[0408] In addition, the server continuously monitors sleep data and generates alerts to warn of health risks if abnormal data patterns are detected. These alerts are automatically sent to the user's smartphone, prompting them to consult a medical professional if necessary.

[0409] Furthermore, the device reviews the user's sleep data and daily schedule each morning to provide personalized advice to maximize activity efficiency. This advice goes beyond mere recommendations, serving as a guide for the user to have a healthy and productive day.

[0410] As a concrete example, suppose a user experiences a sudden increase in heart rate during normal sleep. In this case, the server immediately detects this anomaly and sends an alert to the user. Simultaneously, the device adjusts the firmness and temperature of the bed to help the user transition from deep sleep to light sleep. The following morning, the user is provided with advice on how to improve their activity efficiency, taking this anomaly into account. Thus, the present invention is a system that comprehensively supports the quality of a user's sleep.

[0411] The following describes the processing flow.

[0412] Step 1:

[0413] The device acquires brain waves, heart rate, breathing patterns, and room temperature and illuminance from sleep sensors. This data is collected in real time at regular intervals.

[0414] Step 2:

[0415] The device transmits the acquired biometric data to the server. The data is encrypted and sent through a secure communication channel.

[0416] Step 3:

[0417] The server processes the received data through an analysis algorithm to determine the sleep stage (light sleep, deep sleep, REM sleep). This analysis also takes into account past sleep data to derive the optimal analysis result.

[0418] Step 4:

[0419] The server calculates the optimal environment settings based on the analysis results. These settings include bed hardness, temperature, and incline.

[0420] Step 5:

[0421] The terminal receives the environment settings sent from the server and adjusts the bed's physical mechanisms. Specifically, it activates the built-in motors and temperature control devices to apply the settings.

[0422] Step 6:

[0423] The server continuously monitors the data and sends an alert to the user if it detects an abnormal data pattern that exceeds a specified range (e.g., a sudden increase in heart rate). The alert is displayed on a smartphone or bedside display.

[0424] Step 7:

[0425] The server organizes sleep data and stores it in a secure database. The data from the current day is used to make suggestions for the following morning.

[0426] Step 8:

[0427] When the user wakes up, the device generates and displays personalized activity improvement suggestions based on sleep data and the day's schedule. These suggestions may include specific action plans.

[0428] (Example 1)

[0429] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0430] Conventional sleep support systems fail to effectively utilize users' biometric and environmental information to provide an optimal sleep environment for each individual user. Furthermore, they struggle to quickly detect abnormal health conditions and take appropriate measures.

[0431] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0432] In this invention, the server includes means for acquiring biological and external information in real time, means for analyzing the acquired information and determining the sleep state, and means for adjusting physical conditions according to the determined state. This makes it possible to provide the user with an optimal sleep environment and to quickly detect and respond to abnormalities in their health.

[0433] "Biometric information" is a general term for data related to an individual's physical state, such as brain activity, heart rate information, and respiratory information.

[0434] "External information" is a general term for data related to external conditions such as the indoor environment.

[0435] A "real-time data acquisition device" is a device that can measure and process data instantly.

[0436] A "device that analyzes and determines sleep state" is a device that analyzes acquired data to determine the user's current sleep state (e.g., REM sleep, non-REM sleep).

[0437] A "device for adjusting physical conditions" is a device for automatically optimizing the physical environment of bedding, such as its hardness, temperature, and angle.

[0438] A "health monitoring and abnormality detection device" is a device that monitors a user's biometric information and immediately detects any health-related abnormalities.

[0439] A "device that generates suggestions to improve activity efficiency" is a device that, based on sleep data, creates advice to help users effectively carry out their activities after waking up.

[0440] This invention is a system that acquires biometric and external information in real time during a user's sleep and provides an optimal sleep environment based on that information. The main components include a terminal composed of sensors for acquiring data, a server that analyzes the collected data and provides appropriate instructions, and a user interface for providing feedback to the user.

[0441] The device includes multiple sensors attached to the user's sleep environment. These sensors consist of an electroencephalogram (EEG) sensor to measure brain activity, a heart rate sensor to record heart rate information, a respiratory sensor to monitor breathing information, and room temperature and light sensors to acquire external information. This information is collected in real time and transmitted to a server using wireless technology.

[0442] The server receives the collected data and performs analysis using a generative AI model. This analysis allows the server to determine the user's sleep state (REM sleep, non-REM sleep, etc.). It then calculates the physical conditions necessary to provide the optimal sleep environment. These calculations include parameters for automatically adjusting the bed's hardness, temperature, and angle.

[0443] Furthermore, the server continuously monitors biometric information to detect anomalies, and if an abnormal condition is detected, it quickly sends an alert to the user. This alert is immediately sent to the user's smartphone or other device.

[0444] In addition, the device provides recommendations to improve the user's activity level each morning before they wake up. These recommendations are based on the previous night's sleep data, as well as the day's schedule and expected activities.

[0445] For example, if a user's heart rate abnormally increases during normal sleep, the server detects this information and sends an alert to the user. Simultaneously, the device uses an environmental adjustment device to change the hardness and temperature of the bed, transitioning the user from deep sleep to light sleep. This series of actions allows the user to continue sleeping with peace of mind.

[0446] An example of a prompt can be the text "Generate an appropriate alert message when an abnormal heart rate is detected." This prompt will cause the generation AI model to create an appropriate response.

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] The terminal acquires biometric and external information using sensors attached to the user's bed. Specific inputs include electroencephalogram (EEG), heart rate, respiratory pattern, room temperature, and illuminance. An integrated circuit and data conversion unit within the terminal convert the analog signals from these sensors into digital data. The resulting output is real-time digital data, transmitted to a server via wireless communication technology.

[0450] Step 2:

[0451] The server receives digital data transmitted from the terminal. The input data consists of real-time biometric and environmental data. The server inputs this data into a generating AI model to analyze sleep states. Data processing includes statistical methods and time series analysis, and the output determines the user's sleep stage (e.g., REM sleep or non-REM sleep).

[0452] Step 3:

[0453] The server calculates environmental adjustment parameters based on the determined sleep stage. The input is the sleep stage obtained in the previous step. The output is instructions for adjusting the bed's hardness, temperature, and angle. The server utilizes machine learning algorithms to perform comparative analysis with past data and derive the optimal environmental parameters.

[0454] Step 4:

[0455] The terminal executes environmental adjustment instructions received from the server. The input consists of environmental adjustment parameters from the server. The terminal operates its built-in motor and temperature control unit to adjust the bed's hardness and temperature. The output is a sleep environment optimized for the user's current sleep stage.

[0456] Step 5:

[0457] The server continuously monitors the user's biometric data while they sleep. The input is real-time biometric data. The server runs an anomaly detection algorithm to identify abnormal values, and if there is a health risk, it generates an alert as output. This alert is immediately sent to the user's smart device.

[0458] Step 6:

[0459] The device prepares advice for the user each morning before they wake up, helping them improve their activity efficiency. Inputs include sleep data from the previous night and information about their schedule for the day. Using data analysis tools, it generates helpful suggestions for the user's daily activities. The output is advice provided to the user, enabling them to have a healthy and efficient day.

[0460] (Application Example 1)

[0461] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0462] In modern society, many people suffer from poor sleep quality due to irregular lifestyles and stress. This leads to health problems and decreased productivity. A particular challenge is the lack of a system that optimizes sleep environments according to individual lifestyles and proposes healthy lifestyle habits.

[0463] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0464] In this invention, the server includes a sensor means for acquiring biological data during sleep in real time, an analysis means for analyzing the acquired biological data and determining the sleep stage, an environment adjustment means for adjusting the physical environment according to the determined sleep stage, and a recommendation generation means for generating recommendations to optimize lifestyle habits based on lifestyle data. This makes it possible to individually optimize the user's sleep environment and lifestyle habits, thereby improving their health and quality of life.

[0465] A "sensor" is a device that acquires biological data and external environmental data in real time during sleep.

[0466] "Analysis means" refers to a system that analyzes acquired biometric data and has the function of determining the user's sleep stage.

[0467] An "environmental adjustment device" is a device equipped with a function to adjust the physical environment according to the determined sleep stage.

[0468] A "health monitoring system" is a means of monitoring a user's health status based on sleep data and detecting abnormalities.

[0469] A "proposal generation means" is a system that has the function of generating suggestions to improve the efficiency of the day's activities upon waking up.

[0470] A "recommendation generation method" is a system that generates recommendations to optimize lifestyle habits based on lifestyle data.

[0471] The system implementing this invention collects and analyzes biometric data in real time during the user's sleep, thereby proposing an optimized sleep environment and lifestyle improvements for each individual user. The system has the following configuration.

[0472] The server uses machine learning algorithms to analyze biometric data transmitted from sensor devices and determine the user's sleep stage. This analysis includes data aggregation and filtering, as well as pattern recognition over time. Based on the analyzed data, the environmental adjustment system adjusts the bed's firmness, temperature, and tilt to provide an optimal sleep environment. This ensures that the optimal environment is tailored to each sleep stage.

[0473] Familiar smartphones and smartwatches function as sensors, monitoring brain waves, heart rate, breathing patterns, and the external environment in real time. This allows the server to detect changes in the user's health status and generate alerts through health monitoring devices if abnormalities are detected.

[0474] Furthermore, the server, through its suggestion generation mechanism, provides specific feedback based on the user's sleep data to maximize their activity efficiency the following day. These suggestions contribute to improving the user's daily life.

[0475] For example, if fatigue is detected in a user's sleep data for a given day, the server will notify the user that relaxation is needed in their schedule for the next day, thereby encouraging healthy lifestyle habits. A generative AI model is used to generate these instructions, employing a prompt such as: "Generate relaxing daily activity suggestions based on the user's sleep data."

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] The device uses a smartwatch or smartphone to acquire data on the user's brainwaves, heart rate, breathing patterns, and external environmental data such as room temperature and illuminance. This data is collected in real time through sensors. Biometric data is collected as input and sent to a server.

[0479] Step 2:

[0480] The server analyzes the received biometric data. Using machine learning algorithms, it determines the user's sleep stage from the biometric data. The input is the biometric data collected in step 1, and the output here is the determination of the current sleep stage. Data processing includes time-series analysis and clustering.

[0481] Step 3:

[0482] The server calculates the optimal environmental adjustment parameters based on the determined sleep stage. The input is information about the sleep stage, and the output is the environmental adjustment parameters. The generated parameters are sent to the terminal, which automatically adjusts the firmness, temperature, and tilt of the bed.

[0483] Step 4:

[0484] The server continues to monitor the data and, as a health monitoring tool, immediately generates an alert if an abnormal pattern is detected. The input is continuously collected biometric data, and the output is an alert indicating an anomaly, such as a sudden increase in heart rate.

[0485] Step 5:

[0486] After the user has finished sleeping, the server uses the sleep data to generate specific lifestyle improvement suggestions using a suggestion generation mechanism. The input data is the user's sleep history, and the output is suggestions for lifestyle improvement. In this process, a generation AI model is used to analyze prompt sentences and provide recommendations regarding daily activities for the following day.

[0487] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0488] This invention is a system for comprehensively analyzing a user's biometric data and emotional state during sleep to provide an optimal sleep environment. This system, by combining multiple means as described below, provides the user with high-quality sleep and improves their activity efficiency after waking.

[0489] First, the device uses a group of sensors installed on the bed to acquire data on the user's brainwaves, heart rate, breathing patterns, and external environmental data such as room temperature and illuminance. This data is transmitted to the server in real time. An emotion engine is also built in, which analyzes the user's emotional state from their voice and facial expressions. Emotional data is transmitted along with biometric data.

[0490] The server uses machine learning algorithms to analyze the received biometric and emotional data. This determines the user's sleep stage and then decides on the optimal environment, taking their emotional state into consideration. Based on these results, the server calculates environmental adjustment parameters and sends instructions to the terminal.

[0491] Next, the device adjusts the physical characteristics of the bed (firmness, temperature, and tilt) according to the settings sent from the server. This provides an optimal environment tailored to the user's emotions and sleep stage. For example, if the user is sleeping under stress, the environment is fine-tuned to enhance relaxation.

[0492] In addition, the server continuously monitors sleep data and, if any abnormalities are detected, generates an alert to warn of health risks. This alert is automatically sent to the user's smartphone, prompting them to consult a medical professional.

[0493] Furthermore, each morning, the device provides personalized advice to maximize activity efficiency based on the user's sleep data, emotional data, and daily schedule. For example, if the user's emotional state the previous night was stressful, it will recommend exercise to reduce stress. This allows the user to have a healthy and efficient day.

[0494] For example, if a user feels anxious when falling asleep, the emotion engine detects this, and the server adjusts the environment to promote relaxation. At this time, the device adjusts the bed to be slightly softer and lowers the room temperature to a comfortable level. As a result, the user can enter a calmer state and achieve higher quality sleep. Thus, the present invention is a system that comprehensively utilizes the user's biometric and emotional data to optimize the sleep environment and activity efficiency.

[0495] The following describes the processing flow.

[0496] Step 1:

[0497] The device uses a group of sensors installed on the bed to acquire biometric data such as brain waves, heart rate, breathing patterns, temperature, and illuminance, as well as environmental data, in real time. In addition, it uses a built-in emotion engine to analyze the user's voice and facial expressions to acquire emotional data.

[0498] Step 2:

[0499] The device transmits acquired biometric and emotional data to the server. The data is transferred in real time and serves as foundational data for analysis.

[0500] Step 3:

[0501] The server analyzes the received data in real time and uses machine learning algorithms to determine the user's sleep stage. It also analyzes the user's current emotional state based on emotional data.

[0502] Step 4:

[0503] The server calculates the optimal sleep environment based on the analysis results. It takes into account sleep stages and emotional states and generates adjustment parameters for bed firmness, temperature, and tilt.

[0504] Step 5:

[0505] The device receives adjustment parameters sent from the server and automatically adjusts the physical properties of the bed. This provides an environment suited to the user's sleep stage and emotional state.

[0506] Step 6:

[0507] The server continuously monitors sleep data and immediately issues an alert if it detects any abnormalities, such as a sudden increase in heart rate. The alert is displayed on the user's mobile device or bedside display.

[0508] Step 7:

[0509] The server organizes the data from the night and saves it to a historical database. The sleep and emotional data from that day are used to form suggestions the following morning.

[0510] Step 8:

[0511] When the user wakes up, the device generates and displays suggestions for improving activity efficiency based on the previous night's sleep and emotional state. These suggestions may include specific action plans, such as stress reduction strategies and recommended break times.

[0512] (Example 2)

[0513] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0514] Conventional sleep management systems are limited to acquiring biometric information and simple environmental settings, and are insufficient in providing an optimal sleep environment that takes into account the user's emotional state. Furthermore, they have limitations in detecting abnormalities and providing individualized instructions after waking up. It is necessary to address these challenges and achieve higher quality sleep and improved activity efficiency after waking.

[0515] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0516] In this invention, the server includes information acquisition means for acquiring biometric and environmental information in real time, information analysis means for determining sleep stages and analyzing emotional states using the acquired information, and adjustment means for dynamically adjusting the physical environment based on the analyzed sleep stages and emotional states. This makes it possible to provide an optimal sleep environment according to the user's emotional state, detect anomalies, and improve the efficiency of activities after waking up.

[0517] "Biometric information" refers to data obtained from the user's body, including, for example, brain waves, heart rate, and breathing patterns.

[0518] "Environmental information" refers to data about the physical conditions surrounding the user, including, for example, temperature, humidity, and illuminance.

[0519] "Information acquisition means" refers to devices and mechanisms for collecting biological and environmental information in real time.

[0520] "Information analysis means" refers to analysis devices and algorithms that use acquired biometric and environmental information to determine the user's sleep stage and emotional state.

[0521] "Adjustment means" refers to devices or systems that dynamically change the physical environment based on the analysis results.

[0522] "Health monitoring means" refers to a system that continuously monitors data during sleep and detects abnormalities.

[0523] "Instruction generation means" refers to devices or technologies for generating instructions to improve activity efficiency upon waking.

[0524] The system based on this invention collects and analyzes biometric and environmental information to provide users with an optimal sleep environment in order to achieve high-quality sleep. This system mainly consists of terminals and a server.

[0525] The device acquires the user's biometric information in real time using various sensors installed on the bed. Specific examples of sensors used include electroencephalogram (EEG), heart rate, and respiratory pattern sensors. External environmental information is also acquired using temperature, humidity, and light sensors. Some parts of the device incorporate an emotion engine that analyzes voice data and facial images to evaluate the user's emotional state. The acquired data is then transmitted directly to the server.

[0526] The server analyzes the received biometric and environmental information using machine learning algorithms. Specifically, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used to determine the user's sleep stage and emotional state. Based on this analysis, the server calculates the optimal environmental settings and sends instructions to the terminal.

[0527] For example, if a user falls asleep feeling stressed, the emotion engine detects this state, and the server adjusts settings to enhance relaxation. The device then adjusts the bed's firmness and controls the room temperature to an appropriate level. This allows the user to fall into a smoother, higher-quality sleep.

[0528] Furthermore, this system can monitor sleep abnormalities and, if detected, send alerts to the user via a smartphone or other device. Upon waking, advice is provided using a generative AI model based on the previous night's sleep data and emotional state, to maximize the day's activity efficiency. An example of a prompt for the generative AI model is, "If the user's emotional state the previous day was anxious, what advice should you provide?"

[0529] In this way, the system actively supports the user's health and helps them achieve a better life.

[0530] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0531] Step 1:

[0532] The terminal acquires biometric and environmental information in real time from various sensors installed on the bed. Inputs include data such as brain waves, heart rate, and respiratory patterns, to which temperature, humidity, and illuminance data are added. This information is converted into digital signals and the acquired data is immediately transmitted to the server.

[0533] Step 2:

[0534] The server receives biometric and emotional information transmitted from the terminal. The data received as input is analyzed using machine learning algorithms, particularly CNNs and RNNs. Here, the data is processed to determine the user's sleep stage and emotional state, and the analysis results containing this information are obtained as output. Specifically, this involves a process of analyzing the data while adjusting the model parameters.

[0535] Step 3:

[0536] The server determines the physical environment settings based on the analysis results and calculates environmental parameters to send to the terminal. This step considers the analyzed sleep stages and emotional states as input. The resulting calculated environmental settings (temperature, hardness, tilt, etc.) are output and sent to the terminal. The process involves generating control commands to provide the optimal environment.

[0537] Step 4:

[0538] The terminal adjusts the physical properties of the bed based on environment setting parameters received from the server. Inputs include control commands sent from the server, and outputs include actual environmental changes (e.g., adjustment of bed hardness, temperature changes). Specific operations include using devices such as linear actuators to perform physical adjustments.

[0539] Step 5:

[0540] The server continuously monitors the user's sleep data and generates alerts if anomalies are detected. It receives continuously collected sleep data as input and generates warning messages as output by detecting anomalies. Its operation includes real-time data monitoring and the execution of comparison algorithms.

[0541] Step 6:

[0542] Every morning, the device considers the user's sleep and emotional data and uses a generative AI model to provide advice to maximize activity efficiency. Past sleep data and daily schedules are considered as input, and personalized suggestions are generated as output. Specific operations include selecting suggestions and notifying the user.

[0543] (Application Example 2)

[0544] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0545] In modern society, poor sleep quality negatively impacts health and activity efficiency. To address this problem, it is necessary to comprehensively analyze biometric data and emotional states during sleep and provide an optimal sleep environment. Conventional systems focus on determining sleep stages, but do not adequately adjust the environment considering emotional states. Furthermore, there is a lack of anomaly detection to prevent health risks and suggestions to improve activity efficiency. Therefore, a system that can comprehensively manage these aspects is needed.

[0546] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0547] In this invention, the server includes adjustment means for determining emotional state and optimizing environmental settings, environment modification means for adjusting the physical environment according to the determined sleep stage and emotional state, and health management means for monitoring health status based on sleep information and detecting abnormalities. This makes it possible to provide an optimal sleep environment tailored to the individual needs of the user, enable early detection of health risks, and offer suggestions to support efficient activities.

[0548] A "sensing device" is a device that collects biometric information such as brain waves, heart rate, and breathing patterns from the user in real time while they are sleeping, and also acquires information about the external environment.

[0549] The "analysis means" refers to the function responsible for the process of determining the user's sleep stage based on acquired biometric information.

[0550] "Adjustment mechanisms" refer to functions that evaluate emotional states and optimize environmental settings based on that information.

[0551] The "environmental modification means" is a device that automatically adjusts the hardness, temperature, and angle of the lying surface according to the determined sleep stage and emotional state.

[0552] A "health management system" is a mechanism for continuously monitoring health status based on sleep information and taking appropriate action when an abnormality is detected.

[0553] The "proposal creation method" is a function that generates and provides specific suggestions to maximize the efficiency of the day's activities when the user wakes up.

[0554] The system for realizing this application primarily consists of sensing means, analysis means, adjustment means, environment modification means, health management means, and proposal generation means. Each means is implemented specifically as follows.

[0555] The server uses machine learning algorithms such as Python's Scikit-learn to analyze biometric information and external environmental information received from sensing devices. This allows it to determine the user's sleep stage and further evaluate their emotional state based on data such as their voice and facial expressions.

[0556] Based on the information it has assessed, the terminal automatically determines environmental settings tailored to the user's emotional state via an adjustment mechanism. This data is transmitted from a server, and the terminal uses an environmental modification mechanism to adjust the hardness, temperature, and angle of the sleeping surface, thereby creating an optimal sleep environment. The necessary hardware for this includes a bed with a variable structure and room temperature control equipment.

[0557] Furthermore, the server will continue to monitor the user's health status during sleep using health management tools, and will notify the user's smartphone if any abnormalities are detected. This notification will prompt the user to consult a medical professional if necessary.

[0558] Furthermore, upon waking, users are provided with specific suggestions for improving their activity efficiency, generated by the suggestion generation system. This includes, for example, recommendations for exercise or relaxation if the user experienced high levels of stress the previous day.

[0559] For example, if anxiety is detected before sleep, the server will change settings to promote relaxation, the device will adjust the bed firmness, and the room temperature will be set to a comfortable level. Through this process, the user can get higher quality sleep.

[0560] An example of a prompt message is: "Based on the user's sleep data, please provide the optimal environment. In particular, please tell me how to handle situations where the user is feeling anxious."

[0561] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0562] Step 1:

[0563] The server receives the user's brainwaves, heart rate, breathing pattern, and external environment information transmitted from the sensing device as input data. Based on this biometric information, it uses a machine learning model with Scikit-learn to determine the sleep stage. The current sleep stage is generated as output.

[0564] Step 2:

[0565] The server applies an emotion recognition model to analyze the user's voice and facial image data to determine their emotional state. This analysis treats the emotional state (e.g., stress, anxiety, relaxation) as input data, and the emotional state is obtained as output.

[0566] Step 3:

[0567] The server combines the sleep stages and emotional states obtained in Steps 1 and 2 to determine appropriate environmental settings. Using prompts from the generative AI model, it calculates the specific setting parameters to be set by the environmental modification device and sends them to the terminal. The output is instruction data for environmental adjustment.

[0568] Step 4:

[0569] Based on instruction data received from the server, the terminal uses environmental modification mechanisms to automatically adjust the hardness of the sleeping surface, the room temperature, and the angle. This makes specific physical changes to provide an optimal sleeping environment.

[0570] Step 5:

[0571] The server continuously monitors biometric information during sleep using health management tools. If an abnormality is detected, it generates an alert and sends a notification about the health risk to the user's smartphone. The output is the notification information for the abnormality.

[0572] Step 6:

[0573] When a user wakes up, the server provides their device or smartphone with suggestions to improve their activity efficiency, generated through a suggestion generation system. These suggestions include specific actions (e.g., exercise, relaxation activities) based on the previous day's sleep and emotional data. The output is the content of these suggestions.

[0574] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0575] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0576] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0577] [Fourth Embodiment]

[0578] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0579] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0580] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0581] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0582] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0583] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0584] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0585] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0586] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0587] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0588] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0589] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0590] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0591] This invention is a system for acquiring biometric data in real time during a user's sleep and providing an optimal sleep environment based on that data. This system consists of several key components that work together to provide the user with high-quality sleep and improve their activity efficiency after waking up.

[0592] First, the device acquires data on the user's brainwaves, heart rate, breathing patterns, and external environmental data such as room temperature and illuminance through a group of sensors installed on the bed. This data is transmitted to the server in real time.

[0593] The server utilizes machine learning algorithms to analyze the received biometric data. These algorithms accurately determine the user's sleep stage and provide a basis for determining the most suitable sleep environment. Based on these results, the server calculates environmental adjustment parameters and sends instructions back to the terminal.

[0594] Next, the device adjusts the physical characteristics of the bed (firmness, temperature, and tilt) according to instructions from the server. This allows the user to rest in an optimal environment for each stage of sleep. Because this adjustment is made flexibly according to the depth of sleep, the user can enjoy higher quality sleep.

[0595] In addition, the server continuously monitors sleep data and generates alerts to warn of health risks if abnormal data patterns are detected. These alerts are automatically sent to the user's smartphone, prompting them to consult a medical professional if necessary.

[0596] Furthermore, the device reviews the user's sleep data and daily schedule each morning to provide personalized advice to maximize activity efficiency. This advice goes beyond mere recommendations, serving as a guide for the user to have a healthy and productive day.

[0597] As a concrete example, suppose a user experiences a sudden increase in heart rate during normal sleep. In this case, the server immediately detects this anomaly and sends an alert to the user. Simultaneously, the device adjusts the firmness and temperature of the bed to help the user transition from deep sleep to light sleep. The following morning, the user is provided with advice on how to improve their activity efficiency, taking this anomaly into account. Thus, the present invention is a system that comprehensively supports the quality of a user's sleep.

[0598] The following describes the processing flow.

[0599] Step 1:

[0600] The device acquires brain waves, heart rate, breathing patterns, and room temperature and illuminance from sleep sensors. This data is collected in real time at regular intervals.

[0601] Step 2:

[0602] The device transmits the acquired biometric data to the server. The data is encrypted and sent through a secure communication channel.

[0603] Step 3:

[0604] The server processes the received data through an analysis algorithm to determine the sleep stage (light sleep, deep sleep, REM sleep). This analysis also takes into account past sleep data to derive the optimal analysis result.

[0605] Step 4:

[0606] The server calculates the optimal environment settings based on the analysis results. These settings include bed hardness, temperature, and incline.

[0607] Step 5:

[0608] The terminal receives the environment settings sent from the server and adjusts the bed's physical mechanisms. Specifically, it activates the built-in motors and temperature control devices to apply the settings.

[0609] Step 6:

[0610] The server continuously monitors the data and sends an alert to the user if it detects an abnormal data pattern that exceeds a specified range (e.g., a sudden increase in heart rate). The alert is displayed on a smartphone or bedside display.

[0611] Step 7:

[0612] The server organizes sleep data and stores it in a secure database. The data from the current day is used to make suggestions for the following morning.

[0613] Step 8:

[0614] When the user wakes up, the device generates and displays personalized activity improvement suggestions based on sleep data and the day's schedule. These suggestions may include specific action plans.

[0615] (Example 1)

[0616] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0617] Conventional sleep support systems fail to effectively utilize users' biometric and environmental information to provide an optimal sleep environment for each individual user. Furthermore, they struggle to quickly detect abnormal health conditions and take appropriate measures.

[0618] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0619] In this invention, the server includes means for acquiring biological and external information in real time, means for analyzing the acquired information and determining the sleep state, and means for adjusting physical conditions according to the determined state. This makes it possible to provide the user with an optimal sleep environment and to quickly detect and respond to abnormalities in their health.

[0620] "Biometric information" is a general term for data related to an individual's physical state, such as brain activity, heart rate information, and respiratory information.

[0621] "External information" is a general term for data related to external conditions such as the indoor environment.

[0622] A "real-time data acquisition device" is a device that can measure and process data instantly.

[0623] A "device that analyzes and determines sleep state" is a device that analyzes acquired data to determine the user's current sleep state (e.g., REM sleep, non-REM sleep).

[0624] A "device for adjusting physical conditions" is a device for automatically optimizing the physical environment of bedding, such as its hardness, temperature, and angle.

[0625] A "health monitoring and abnormality detection device" is a device that monitors a user's biometric information and immediately detects any health-related abnormalities.

[0626] A "device that generates suggestions to improve activity efficiency" is a device that, based on sleep data, creates advice to help users effectively carry out their activities after waking up.

[0627] This invention is a system that acquires biometric and external information in real time during a user's sleep and provides an optimal sleep environment based on that information. The main components include a terminal composed of sensors for acquiring data, a server that analyzes the collected data and provides appropriate instructions, and a user interface for providing feedback to the user.

[0628] The device includes multiple sensors attached to the user's sleep environment. These sensors consist of an electroencephalogram (EEG) sensor to measure brain activity, a heart rate sensor to record heart rate information, a respiratory sensor to monitor breathing information, and room temperature and light sensors to acquire external information. This information is collected in real time and transmitted to a server using wireless technology.

[0629] The server receives the collected data and performs analysis using a generative AI model. This analysis allows the server to determine the user's sleep state (REM sleep, non-REM sleep, etc.). It then calculates the physical conditions necessary to provide the optimal sleep environment. These calculations include parameters for automatically adjusting the bed's hardness, temperature, and angle.

[0630] Furthermore, the server continuously monitors biometric information to detect anomalies, and if an abnormal condition is detected, it quickly sends an alert to the user. This alert is immediately sent to the user's smartphone or other device.

[0631] In addition, the device provides recommendations to improve the user's activity level each morning before they wake up. These recommendations are based on the previous night's sleep data, as well as the day's schedule and expected activities.

[0632] For example, if a user's heart rate abnormally increases during normal sleep, the server detects this information and sends an alert to the user. Simultaneously, the device uses an environmental adjustment device to change the hardness and temperature of the bed, transitioning the user from deep sleep to light sleep. This series of actions allows the user to continue sleeping with peace of mind.

[0633] An example of a prompt can be the text "Generate an appropriate alert message when an abnormal heart rate is detected." This prompt will cause the generation AI model to create an appropriate response.

[0634] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0635] Step 1:

[0636] The terminal acquires biometric and external information using sensors attached to the user's bed. Specific inputs include electroencephalogram (EEG), heart rate, respiratory pattern, room temperature, and illuminance. An integrated circuit and data conversion unit within the terminal convert the analog signals from these sensors into digital data. The resulting output is real-time digital data, transmitted to a server via wireless communication technology.

[0637] Step 2:

[0638] The server receives digital data transmitted from the terminal. The input data consists of real-time biometric and environmental data. The server inputs this data into a generating AI model to analyze sleep states. Data processing includes statistical methods and time series analysis, and the output determines the user's sleep stage (e.g., REM sleep or non-REM sleep).

[0639] Step 3:

[0640] The server calculates environmental adjustment parameters based on the determined sleep stage. The input is the sleep stage obtained in the previous step. The output is instructions for adjusting the bed's hardness, temperature, and angle. The server utilizes machine learning algorithms to perform comparative analysis with past data and derive the optimal environmental parameters.

[0641] Step 4:

[0642] The terminal executes environmental adjustment instructions received from the server. The input consists of environmental adjustment parameters from the server. The terminal operates its built-in motor and temperature control unit to adjust the bed's hardness and temperature. The output is a sleep environment optimized for the user's current sleep stage.

[0643] Step 5:

[0644] The server continuously monitors the user's biometric data while they sleep. The input is real-time biometric data. The server runs an anomaly detection algorithm to identify abnormal values, and if there is a health risk, it generates an alert as output. This alert is immediately sent to the user's smart device.

[0645] Step 6:

[0646] The device prepares advice for the user each morning before they wake up, helping them improve their activity efficiency. Inputs include sleep data from the previous night and information about their schedule for the day. Using data analysis tools, it generates helpful suggestions for the user's daily activities. The output is advice provided to the user, enabling them to have a healthy and efficient day.

[0647] (Application Example 1)

[0648] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0649] In modern society, many people suffer from poor sleep quality due to irregular lifestyles and stress. This leads to health problems and decreased productivity. A particular challenge is the lack of a system that optimizes sleep environments according to individual lifestyles and proposes healthy lifestyle habits.

[0650] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0651] In this invention, the server includes a sensor means for acquiring biological data during sleep in real time, an analysis means for analyzing the acquired biological data and determining the sleep stage, an environment adjustment means for adjusting the physical environment according to the determined sleep stage, and a recommendation generation means for generating recommendations to optimize lifestyle habits based on lifestyle data. This makes it possible to individually optimize the user's sleep environment and lifestyle habits, thereby improving their health and quality of life.

[0652] A "sensor" is a device that acquires biological data and external environmental data in real time during sleep.

[0653] "Analysis means" refers to a system that analyzes acquired biometric data and has the function of determining the user's sleep stage.

[0654] An "environmental adjustment device" is a device equipped with a function to adjust the physical environment according to the determined sleep stage.

[0655] A "health monitoring system" is a means of monitoring a user's health status based on sleep data and detecting abnormalities.

[0656] A "proposal generation means" is a system that has the function of generating suggestions to improve the efficiency of the day's activities upon waking up.

[0657] A "recommendation generation method" is a system that generates recommendations to optimize lifestyle habits based on lifestyle data.

[0658] The system implementing this invention collects and analyzes biometric data in real time during the user's sleep, thereby proposing an optimized sleep environment and lifestyle improvements for each individual user. The system has the following configuration.

[0659] The server uses machine learning algorithms to analyze biometric data transmitted from sensor devices and determine the user's sleep stage. This analysis includes data aggregation and filtering, as well as pattern recognition over time. Based on the analyzed data, the environmental adjustment system adjusts the bed's firmness, temperature, and tilt to provide an optimal sleep environment. This ensures that the optimal environment is tailored to each sleep stage.

[0660] Familiar smartphones and smartwatches function as sensors, monitoring brain waves, heart rate, breathing patterns, and the external environment in real time. This allows the server to detect changes in the user's health status and generate alerts through health monitoring devices if abnormalities are detected.

[0661] Furthermore, the server, through its suggestion generation mechanism, provides specific feedback based on the user's sleep data to maximize their activity efficiency the following day. These suggestions contribute to improving the user's daily life.

[0662] For example, if fatigue is detected in a user's sleep data for a given day, the server will notify the user that relaxation is needed in their schedule for the next day, thereby encouraging healthy lifestyle habits. A generative AI model is used to generate these instructions, employing a prompt such as: "Generate relaxing daily activity suggestions based on the user's sleep data."

[0663] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0664] Step 1:

[0665] The device uses a smartwatch or smartphone to acquire data on the user's brainwaves, heart rate, breathing patterns, and external environmental data such as room temperature and illuminance. This data is collected in real time through sensors. Biometric data is collected as input and sent to a server.

[0666] Step 2:

[0667] The server analyzes the received biometric data. Using machine learning algorithms, it determines the user's sleep stage from the biometric data. The input is the biometric data collected in step 1, and the output here is the determination of the current sleep stage. Data processing includes time-series analysis and clustering.

[0668] Step 3:

[0669] The server calculates the optimal environmental adjustment parameters based on the determined sleep stage. The input is information about the sleep stage, and the output is the environmental adjustment parameters. The generated parameters are sent to the terminal, which automatically adjusts the firmness, temperature, and tilt of the bed.

[0670] Step 4:

[0671] The server continues to monitor the data and, as a health monitoring tool, immediately generates an alert if an abnormal pattern is detected. The input is continuously collected biometric data, and the output is an alert indicating an anomaly, such as a sudden increase in heart rate.

[0672] Step 5:

[0673] After the user has finished sleeping, the server uses the sleep data to generate specific lifestyle improvement suggestions using a suggestion generation mechanism. The input data is the user's sleep history, and the output is suggestions for lifestyle improvement. In this process, a generation AI model is used to analyze prompt sentences and provide recommendations regarding daily activities for the following day.

[0674] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0675] This invention is a system for comprehensively analyzing a user's biometric data and emotional state during sleep to provide an optimal sleep environment. This system, by combining multiple means as described below, provides the user with high-quality sleep and improves their activity efficiency after waking.

[0676] First, the device uses a group of sensors installed on the bed to acquire data on the user's brainwaves, heart rate, breathing patterns, and external environmental data such as room temperature and illuminance. This data is transmitted to the server in real time. An emotion engine is also built in, which analyzes the user's emotional state from their voice and facial expressions. Emotional data is transmitted along with biometric data.

[0677] The server uses machine learning algorithms to analyze the received biometric and emotional data. This determines the user's sleep stage and then decides on the optimal environment, taking their emotional state into consideration. Based on these results, the server calculates environmental adjustment parameters and sends instructions to the terminal.

[0678] Next, the device adjusts the physical characteristics of the bed (firmness, temperature, and tilt) according to the settings sent from the server. This provides an optimal environment tailored to the user's emotions and sleep stage. For example, if the user is sleeping under stress, the environment is fine-tuned to enhance relaxation.

[0679] In addition, the server continuously monitors sleep data and, if any abnormalities are detected, generates an alert to warn of health risks. This alert is automatically sent to the user's smartphone, prompting them to consult a medical professional.

[0680] Furthermore, each morning, the device provides personalized advice to maximize activity efficiency based on the user's sleep data, emotional data, and daily schedule. For example, if the user's emotional state the previous night was stressful, it will recommend exercise to reduce stress. This allows the user to have a healthy and efficient day.

[0681] For example, if a user feels anxious when falling asleep, the emotion engine detects this, and the server adjusts the environment to promote relaxation. At this time, the device adjusts the bed to be slightly softer and lowers the room temperature to a comfortable level. As a result, the user can enter a calmer state and achieve higher quality sleep. Thus, the present invention is a system that comprehensively utilizes the user's biometric and emotional data to optimize the sleep environment and activity efficiency.

[0682] The following describes the processing flow.

[0683] Step 1:

[0684] The device uses a group of sensors installed on the bed to acquire biometric data such as brain waves, heart rate, breathing patterns, temperature, and illuminance, as well as environmental data, in real time. In addition, it uses a built-in emotion engine to analyze the user's voice and facial expressions to acquire emotional data.

[0685] Step 2:

[0686] The device transmits acquired biometric and emotional data to the server. The data is transferred in real time and serves as foundational data for analysis.

[0687] Step 3:

[0688] The server analyzes the received data in real time and uses machine learning algorithms to determine the user's sleep stage. It also analyzes the user's current emotional state based on emotional data.

[0689] Step 4:

[0690] The server calculates the optimal sleep environment based on the analysis results. It takes into account sleep stages and emotional states and generates adjustment parameters for bed firmness, temperature, and tilt.

[0691] Step 5:

[0692] The device receives adjustment parameters sent from the server and automatically adjusts the physical properties of the bed. This provides an environment suited to the user's sleep stage and emotional state.

[0693] Step 6:

[0694] The server continuously monitors sleep data and immediately issues an alert if it detects any abnormalities, such as a sudden increase in heart rate. The alert is displayed on the user's mobile device or bedside display.

[0695] Step 7:

[0696] The server organizes the data from the night and saves it to a historical database. The sleep and emotional data from that day are used to form suggestions the following morning.

[0697] Step 8:

[0698] When the user wakes up, the device generates and displays suggestions for improving activity efficiency based on the previous night's sleep and emotional state. These suggestions may include specific action plans, such as stress reduction strategies and recommended break times.

[0699] (Example 2)

[0700] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0701] Conventional sleep management systems are limited to acquiring biometric information and simple environmental settings, and are insufficient in providing an optimal sleep environment that takes into account the user's emotional state. Furthermore, they have limitations in detecting abnormalities and providing individualized instructions after waking up. It is necessary to address these challenges and achieve higher quality sleep and improved activity efficiency after waking.

[0702] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0703] In this invention, the server includes information acquisition means for acquiring biometric and environmental information in real time, information analysis means for determining sleep stages and analyzing emotional states using the acquired information, and adjustment means for dynamically adjusting the physical environment based on the analyzed sleep stages and emotional states. This makes it possible to provide an optimal sleep environment according to the user's emotional state, detect anomalies, and improve the efficiency of activities after waking up.

[0704] "Biometric information" refers to data obtained from the user's body, including, for example, brain waves, heart rate, and breathing patterns.

[0705] "Environmental information" refers to data about the physical conditions surrounding the user, including, for example, temperature, humidity, and illuminance.

[0706] "Information acquisition means" refers to devices and mechanisms for collecting biological and environmental information in real time.

[0707] "Information analysis means" refers to analysis devices and algorithms that use acquired biometric and environmental information to determine the user's sleep stage and emotional state.

[0708] "Adjustment means" refers to devices or systems that dynamically change the physical environment based on the analysis results.

[0709] "Health monitoring means" refers to a system that continuously monitors data during sleep and detects abnormalities.

[0710] "Instruction generation means" refers to devices or technologies for generating instructions to improve activity efficiency upon waking.

[0711] The system based on this invention collects and analyzes biometric and environmental information to provide users with an optimal sleep environment in order to achieve high-quality sleep. This system mainly consists of terminals and a server.

[0712] The device acquires the user's biometric information in real time using various sensors installed on the bed. Specific examples of sensors used include electroencephalogram (EEG), heart rate, and respiratory pattern sensors. External environmental information is also acquired using temperature, humidity, and light sensors. Some parts of the device incorporate an emotion engine that analyzes voice data and facial images to evaluate the user's emotional state. The acquired data is then transmitted directly to the server.

[0713] The server analyzes the received biometric and environmental information using machine learning algorithms. Specifically, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used to determine the user's sleep stage and emotional state. Based on this analysis, the server calculates the optimal environmental settings and sends instructions to the terminal.

[0714] For example, if a user falls asleep feeling stressed, the emotion engine detects this state, and the server adjusts settings to enhance relaxation. The device then adjusts the bed's firmness and controls the room temperature to an appropriate level. This allows the user to fall into a smoother, higher-quality sleep.

[0715] Furthermore, this system can monitor sleep abnormalities and, if detected, send alerts to the user via a smartphone or other device. Upon waking, advice is provided using a generative AI model based on the previous night's sleep data and emotional state, to maximize the day's activity efficiency. An example of a prompt for the generative AI model is, "If the user's emotional state the previous day was anxious, what advice should you provide?"

[0716] In this way, the system actively supports the user's health and helps them achieve a better life.

[0717] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0718] Step 1:

[0719] The terminal acquires biometric and environmental information in real time from various sensors installed on the bed. Inputs include data such as brain waves, heart rate, and respiratory patterns, to which temperature, humidity, and illuminance data are added. This information is converted into digital signals and the acquired data is immediately transmitted to the server.

[0720] Step 2:

[0721] The server receives biometric and emotional information transmitted from the terminal. The data received as input is analyzed using machine learning algorithms, particularly CNNs and RNNs. Here, the data is processed to determine the user's sleep stage and emotional state, and the analysis results containing this information are obtained as output. Specifically, this involves a process of analyzing the data while adjusting the model parameters.

[0722] Step 3:

[0723] The server determines the physical environment settings based on the analysis results and calculates environmental parameters to send to the terminal. This step considers the analyzed sleep stages and emotional states as input. The resulting calculated environmental settings (temperature, hardness, tilt, etc.) are output and sent to the terminal. The process involves generating control commands to provide the optimal environment.

[0724] Step 4:

[0725] The terminal adjusts the physical properties of the bed based on environment setting parameters received from the server. Inputs include control commands sent from the server, and outputs include actual environmental changes (e.g., adjustment of bed hardness, temperature changes). Specific operations include using devices such as linear actuators to perform physical adjustments.

[0726] Step 5:

[0727] The server continuously monitors the user's sleep data and generates alerts if anomalies are detected. It receives continuously collected sleep data as input and generates warning messages as output by detecting anomalies. Its operation includes real-time data monitoring and the execution of comparison algorithms.

[0728] Step 6:

[0729] Every morning, the device considers the user's sleep and emotional data and uses a generative AI model to provide advice to maximize activity efficiency. Past sleep data and daily schedules are considered as input, and personalized suggestions are generated as output. Specific operations include selecting suggestions and notifying the user.

[0730] (Application Example 2)

[0731] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0732] In modern society, poor sleep quality negatively impacts health and activity efficiency. To address this problem, it is necessary to comprehensively analyze biometric data and emotional states during sleep and provide an optimal sleep environment. Conventional systems focus on determining sleep stages, but do not adequately adjust the environment considering emotional states. Furthermore, there is a lack of anomaly detection to prevent health risks and suggestions to improve activity efficiency. Therefore, a system that can comprehensively manage these aspects is needed.

[0733] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0734] In this invention, the server includes adjustment means for determining emotional state and optimizing environmental settings, environment modification means for adjusting the physical environment according to the determined sleep stage and emotional state, and health management means for monitoring health status based on sleep information and detecting abnormalities. This makes it possible to provide an optimal sleep environment tailored to the individual needs of the user, enable early detection of health risks, and offer suggestions to support efficient activities.

[0735] A "sensing device" is a device that collects biometric information such as brain waves, heart rate, and breathing patterns from the user in real time while they are sleeping, and also acquires information about the external environment.

[0736] The "analysis means" refers to the function responsible for the process of determining the user's sleep stage based on acquired biometric information.

[0737] "Adjustment mechanisms" refer to functions that evaluate emotional states and optimize environmental settings based on that information.

[0738] The "environmental modification means" is a device that automatically adjusts the hardness, temperature, and angle of the lying surface according to the determined sleep stage and emotional state.

[0739] A "health management system" is a mechanism for continuously monitoring health status based on sleep information and taking appropriate action when an abnormality is detected.

[0740] The "proposal creation method" is a function that generates and provides specific suggestions to maximize the efficiency of the day's activities when the user wakes up.

[0741] The system for realizing this application primarily consists of sensing means, analysis means, adjustment means, environment modification means, health management means, and proposal generation means. Each means is implemented specifically as follows.

[0742] The server uses machine learning algorithms such as Python's Scikit-learn to analyze biometric information and external environmental information received from sensing devices. This allows it to determine the user's sleep stage and further evaluate their emotional state based on data such as their voice and facial expressions.

[0743] Based on the information it has assessed, the terminal automatically determines environmental settings tailored to the user's emotional state via an adjustment mechanism. This data is transmitted from a server, and the terminal uses an environmental modification mechanism to adjust the hardness, temperature, and angle of the sleeping surface, thereby creating an optimal sleep environment. The necessary hardware for this includes a bed with a variable structure and room temperature control equipment.

[0744] Furthermore, the server will continue to monitor the user's health status during sleep using health management tools, and will notify the user's smartphone if any abnormalities are detected. This notification will prompt the user to consult a medical professional if necessary.

[0745] Furthermore, upon waking, users are provided with specific suggestions for improving their activity efficiency, generated by the suggestion generation system. This includes, for example, recommendations for exercise or relaxation if the user experienced high levels of stress the previous day.

[0746] For example, if anxiety is detected before sleep, the server will change settings to promote relaxation, the device will adjust the bed firmness, and the room temperature will be set to a comfortable level. Through this process, the user can get higher quality sleep.

[0747] An example of a prompt message is: "Based on the user's sleep data, please provide the optimal environment. In particular, please tell me how to handle situations where the user is feeling anxious."

[0748] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0749] Step 1:

[0750] The server receives the user's brainwaves, heart rate, breathing pattern, and external environment information transmitted from the sensing device as input data. Based on this biometric information, it uses a machine learning model with Scikit-learn to determine the sleep stage. The current sleep stage is generated as output.

[0751] Step 2:

[0752] The server applies an emotion recognition model to analyze the user's voice and facial image data to determine their emotional state. This analysis treats the emotional state (e.g., stress, anxiety, relaxation) as input data, and the emotional state is obtained as output.

[0753] Step 3:

[0754] The server combines the sleep stages and emotional states obtained in Steps 1 and 2 to determine appropriate environmental settings. Using prompts from the generative AI model, it calculates the specific setting parameters to be set by the environmental modification device and sends them to the terminal. The output is instruction data for environmental adjustment.

[0755] Step 4:

[0756] Based on instruction data received from the server, the terminal uses environmental modification mechanisms to automatically adjust the hardness of the sleeping surface, the room temperature, and the angle. This makes specific physical changes to provide an optimal sleeping environment.

[0757] Step 5:

[0758] The server continuously monitors biometric information during sleep using health management tools. If an abnormality is detected, it generates an alert and sends a notification about the health risk to the user's smartphone. The output is the notification information for the abnormality.

[0759] Step 6:

[0760] When a user wakes up, the server provides their device or smartphone with suggestions to improve their activity efficiency, generated through a suggestion generation system. These suggestions include specific actions (e.g., exercise, relaxation activities) based on the previous day's sleep and emotional data. The output is the content of these suggestions.

[0761] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0762] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0763] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0764] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0765] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0766] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0767] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0768] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0769] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0770] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0771] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0772] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0773] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0774] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0775] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0776] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0777] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0778] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0779] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0780] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0781] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0782] The following is further disclosed regarding the embodiments described above.

[0783] (Claim 1)

[0784] A sensor means for acquiring biological data during sleep in real time,

[0785] An analytical means for analyzing acquired biological data and determining sleep stages,

[0786] An environmental adjustment means that adjusts the physical environment according to the determined sleep stage,

[0787] A health monitoring system that monitors health status based on sleep data and detects abnormalities,

[0788] A suggestion generation method that generates suggestions to improve the efficiency of the day's activities upon waking up,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, wherein the sensor means acquires electroencephalogram, heart rate, respiratory pattern, and external environmental data.

[0792] (Claim 3)

[0793] The system according to claim 1, wherein the environmental adjustment means has a function to automatically adjust the hardness, temperature, and tilt of the bed.

[0794] "Example 1"

[0795] (Claim 1)

[0796] A device that acquires biological information and external information in real time,

[0797] A device that analyzes acquired information and determines the sleep state,

[0798] A device that adjusts physical conditions according to the determined state,

[0799] A device that monitors health status based on information and detects abnormalities,

[0800] A device that generates suggestions to improve activity efficiency upon waking,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, wherein the device acquires brain activity, heart rate information, respiratory information, and external information.

[0804] (Claim 3)

[0805] The system according to claim 1, wherein the device has a function to automatically adjust the hardness, temperature, and angle of the bedding.

[0806] "Application Example 1"

[0807] (Claim 1)

[0808] A sensor means for acquiring biological data during sleep in real time,

[0809] An analytical means for analyzing acquired biological data and determining sleep stages,

[0810] An environmental adjustment means that adjusts the physical environment according to the determined sleep stage,

[0811] A health monitoring system that monitors health status based on sleep data and detects abnormalities,

[0812] A suggestion generation method that generates suggestions to improve the efficiency of the day's activities upon waking up,

[0813] A recommendation generation method that generates recommendations to optimize lifestyle habits based on lifestyle data,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, wherein the sensor means acquires electroencephalogram, heart rate, respiratory pattern, and external environmental data.

[0817] (Claim 3)

[0818] The system according to claim 1, wherein the environmental adjustment means has a function to automatically adjust the hardness, temperature, and tilt of the bed.

[0819] "Example 2 of combining an emotion engine"

[0820] (Claim 1)

[0821] Information acquisition means for acquiring biological and environmental information in real time,

[0822] An information analysis means that uses the acquired information to determine the sleep stage and analyze the emotional state,

[0823] A means of dynamically adjusting the physical environment based on analyzed sleep stages and emotional states,

[0824] A health monitoring system that continuously monitors data during sleep and detects abnormalities,

[0825] An instruction generation means that generates instructions for improving activity efficiency by considering sleep data and emotional data upon waking,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, wherein the above-mentioned information acquisition means acquires electroencephalogram, heart rate data, respiratory patterns, and external environmental information.

[0829] (Claim 3)

[0830] The system according to claim 1, wherein the above-mentioned adjustment means has the function of automatically adjusting the hardness, temperature, and angle of the bedding.

[0831] "Application example 2 when combining with an emotional engine"

[0832] (Claim 1)

[0833] A sensing means for acquiring biological information during sleep in real time,

[0834] An analytical means for analyzing acquired biological information and determining sleep stages,

[0835] A means of adjusting the environment settings to determine the emotional state,

[0836] Environmental modification means that adjust the physical environment according to the determined sleep stage and emotional state,

[0837] A health management system that monitors health status and detects abnormalities based on sleep information,

[0838] A proposal generation method that generates suggestions to maximize the efficiency of the day's activities upon waking up,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, wherein the sensing means acquires electroencephalogram, heart rate, respiratory pattern, and external environmental information.

[0842] (Claim 3)

[0843] The system according to claim 1, wherein the environmental modification means has a function to automatically adjust the hardness, temperature, and angle of the reclining surface. [Explanation of Symbols]

[0844] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A sensor means for acquiring biological data during sleep in real time, An analytical means for analyzing acquired biological data and determining sleep stages, An environmental adjustment means that adjusts the physical environment according to the determined sleep stage, A health monitoring system that monitors health status based on sleep data and detects abnormalities, A suggestion generation method that generates suggestions to improve the efficiency of the day's activities upon waking up, A recommendation generation method that generates recommendations to optimize lifestyle habits based on lifestyle data, A system that includes this.

2. The system according to claim 1, wherein the sensor means acquires electroencephalogram, heart rate, respiratory pattern, and external environmental data.

3. The system according to claim 1, wherein the environmental adjustment means has a function to automatically adjust the hardness, temperature, and tilt of the bed.

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